In Hannahglover/Glowworm: Mapping genes to single cell RNAseq
Setup
library(Seurat)
library(tidyverse)
library(ggpubr)
library(harmony)
ClusterFunc_All_RNA = function(SeuFile){
Filename = as.character(substitute(SeuFile))
for(y in set.kparam){
for(z in set.dim){
for(v in set.res){
DefaultAssay(SeuFile) = "integrated"
SeuFile <- FindNeighbors(SeuFile, k.param=y, dims=1:z)
SeuFile <- FindClusters(SeuFile, resolution = v)
DefaultAssay(SeuFile) = "RNA"
pdf(paste("ISLETS_", Filename, "_res", v, "_k", y, "_dim", z, "_umapSML.pdf", sep=""), width=12, height=10)
dimplot = DimPlot(SeuFile, reduction="umap", label=T)
print(dimplot)
dev.off()
pdf(paste("ISLETS_", Filename, "_res", v, "_k", y, "_dim", z, "_umapLGE.pdf", sep=""), width=25, height=25)
dimplot = DimPlot(SeuFile, reduction="umap", label=T)
print(dimplot)
dev.off()
GeneLists2 = GeneLists
for(GL in names(GeneLists2)){
GeneLists2[[GL]] = subset(GeneLists2[[GL]], GeneLists2[[GL]] %in% row.names(SeuFile))
}
###VLNS
AllVlns = list()
for(d in names(GeneLists2)){
Genes = GeneLists2[[d]]
StorePlots = list()
for(x in Genes[1]){
plotA <- VlnPlot(SeuFile, features = x, pt.size = 0, same.y.lims = F,)
plotA <- plotA + coord_flip()+ theme(axis.ticks.x= element_blank(), axis.title.y=element_blank(), axis.text.x=element_blank(), axis.title.x=element_blank(),
axis.ticks.y = element_blank(), legend.position = "none", plot.title = element_text(size=12))+ labs(title = d, subtitle = Genes[1])
StorePlots[[x]] = plotA
}
for(x in Genes[2:length(Genes)]){
plotB <- VlnPlot(SeuFile, features = x, pt.size = 0, same.y.lims = F,)
plotB <- plotB + coord_flip()+ theme(axis.ticks.x= element_blank(), axis.title.y=element_blank(), axis.text.x=element_blank(), axis.title.x=element_blank(),
axis.ticks.y = element_blank(), legend.position = "none", axis.text.y = element_blank(), plot.title = element_text(size=12))
StorePlots[[x]] = plotB
}
AllVlns[[d]] <- ggarrange(plotlist = StorePlots, widths=c(1.4, rep(1, length(Genes)-1)), ncol = 60, nrow = 1)
}
pdf(paste("ISLETS_", Filename, "_res", v, "_k", y, "_dim", z, "_AllMultiVlns.pdf", sep=""), width=60, height=length(unique(SeuFile@active.ident)))
print(AllVlns)
dev.off()
#Cell No
CellNo = as.data.frame(table(SeuFile@meta.data$seurat_clusters))
write.csv(CellNo, paste("ISLETS_", Filename, "_counts_k", y, "_dim", z, "_res", v, ".csv", sep=""), row.names = F)
}}
#Feature Plots
FPList = list()
for(d in names(GeneLists2)){
Genes = GeneLists2[[d]]
FPSinglePage = list()
FPSinglePage[[1]] = FeaturePlot(SeuFile, Genes[1], reduction="umap") + labs(title=paste(d, Genes[1]))
for(p in seq(2, length(Genes), 1)){
FPSinglePage[[p]] = FeaturePlot(SeuFile, Genes[p], reduction="umap")
}
FPList[[d]] = ggarrange(plotlist = FPSinglePage, ncol=6, nrow=10)
}
pdf(paste("ISLETS_", Filename, "_dim", z, "_AllFPs.pdf", sep=""), width = 25, height = 45)
print(FPList)
dev.off()
}}
CheckUMAP = function(SeuFilez){
if(class(CheckInput) == "data.frame"){
CheckMeta = as.data.frame(c(rep("RoI", length(row.names(CheckInput)))))
colnames(CheckMeta) = "Pop"
row.names(CheckMeta) = row.names(CheckInput)
}else{
CheckMeta = as.data.frame(c(rep("RoI", length(colnames(CheckInput)))))
colnames(CheckMeta) = "Pop"
row.names(CheckMeta) = colnames(CheckInput)
}
SeuFilez = AddMetaData(SeuFilez, CheckMeta, "CheckMeta")
Idents(SeuFilez) = "CheckMeta"
DimPlot(SeuFilez, reduction="umap")
}
########################################################################
GenerateMetaData = function(ListMeta){
MetaOutput = as.data.frame(matrix(ncol = 1, nrow =0))
for(x in seq(1,length(ListMeta),1)){
if(class(ListMeta[[x]]) == "data.frame"){
Temp = as.data.frame(rep(names(ListMeta)[[x]], length(row.names(ListMeta[[x]]))))
colnames(Temp) = "Pop"
row.names(Temp) = row.names(ListMeta[[x]])
}else{
Temp = as.data.frame(rep(names(ListMeta)[[x]], length(colnames(ListMeta[[x]]))))
colnames(Temp) = "Pop"
row.names(Temp) = colnames(ListMeta[[x]])
}
MetaOutput = rbind(MetaOutput, Temp)
}
return(MetaOutput)
}
GenerateMetaData_Barcodes = function(ListMeta){
MetaOutput = as.data.frame(matrix(ncol = 2, nrow =0))
for(x in seq(1,length(ListMeta),1)){
if(class(ListMeta[[x]]) == "data.frame"){
Temp = as.data.frame(rep(names(ListMeta)[[x]], length(row.names(ListMeta[[x]]))))
colnames(Temp) = "Pop"
Temp$Barcodes = row.names(ListMeta[[x]])
}else{
Temp = as.data.frame(rep(names(ListMeta)[[x]], length(colnames(ListMeta[[x]]))))
colnames(Temp) = "Pop"
Temp$Barcodes = colnames(ListMeta[[x]])
}
MetaOutput = rbind(MetaOutput, Temp)
}
return(MetaOutput)
}
Gene Lists: All Pops
GeneLists = list()
GeneLists[["Zhou_MainMarkers"]] = c("POMC", "VIM", "HES1", "HMGA2", "ARHGAP28", "NES", "ASCL1", "NBPF10", "NBPF15", "PLAGL1", "SFTA3", "STMN2", "SYT1", "SLC32A1", "GAD2", "HDC", "SLC17A6", "PDGFRA", "APOD", "GPR75-ASB3", "MBP", "MOG", "AQP4", "AGT", "FAM107A", "CCDC153", "CCDC74A", "CCDC74B", "LRTOMT", "AIF1", "CX3CR1", "CLDN5", "FLT1", "SLC38A5", "NDUFA4L2", "PDGFRB", "PTGDS", "COL1A1")
GeneLists[["Zhou_MainMarkersV2"]] = c("OLIG1", "OLIG2", "SOX8", "JUNB", "EGR1", "MYC", "OLIG1","OLIG2", "NKX2-2", "FOXJ1", "GLIS3", "AXNA1","TBX2", "TBX3", "LHX2", "RAX", "SHH", "SIX3", "SIX6", "SLC1A3", "NES", "FABP7", "FAM107A", "CD82","TBX3", "RAX", "SIX3", "OTX2", "IRX2", "IRX3", "IRX5", "DLX1", "DLX2", "DLX5", "DLX6", "SOX3", "LHX1", "ONECUT2", "OTP")
GeneLists[["BroadGenes"]] = c("SYT1", "SNAP25", "TUBB3","SYP", #Broad Neuronal
"SLC32A1", "DLX1", "DLX2", "DLX5", "GAD1", #GABAergic
"SLC17A6", "SLC17A8") #Glutaminergic
GeneLists[["HypothalamicProgenSubclass"]] =c("VIM", "ASCL1", #Hypothalamic Progenitor - And/or
"HES1", "MKI67", "FABP7", "TTYH1", "HMGA2", "MEIS2", "SLC1A3", "FAM107A", "NKX2-1", "NHLH2", "DLX1", "OLIG2", "DLX6-AS1", "STMN2", "HOPX", "HES1", "NES", "SOX2", "EGFR", "DLL1", "CLDN10", "FAM107A", "GADD45G", "HES6", "SLC1A2") #Hypothalamic subclasses
GeneLists[["OligList"]] = unique(c("APOD", "PMP22", "MAG", "OLIG1", "PLLP", "CSPG4", "PDGFRA", "NEU4", "TNS3", "FYN", "TCF7L2", "MAL", "MOG", "PLP1", "SERINC5", "TFRC", "OLIG1", "OLIG2", "CNP", "ST8SIA1", "CD9"))
GeneLists[["AstroMicro"]] = unique(c("GJA1", "GFAP", "AQP4", "SLC7A10", "ALDH1L1", "AGT", "SLC1A3", "S100B", "ALDOC", "SLC1A2", "ID3", "ASCL1", "BTG2", "APOE", "C1QB", "AIF1", "P2RY12", "MRC1"))
GeneLists[["EpendyTanyList"]] = c("FOXJ1", "RAX", "ADM", "EMX2", "LHX2", "TMEM212", "CRYM", "CCDC153", "RFX2", "RFX3", "RFX4", "NPAS3", "COL23A1", "CD59", "SLC17A8", "VCAN", "FRZB", "PENK", "COL25A1", "CACNA2D2", "STOML3", "TMEM212", "SLC7A11", "PCP4L1", "PLTP", "TGFB2", "NR2E1", "EPHB1", "P3H2", "NELL2", "FGF10", "RGCC", "GRIA2", "RLBP1", "SIX6", "SCN7A", "MEST", "IGFBP5", "RGS7BP")
GeneLists[["EndoList"]] = c("SLCO1C1", "SLC38A5", "MYH11", "MRC1", "CLDN5", "ITM2A", "FLT1")
GeneLists[["MuralPeriVLMCList"]] = c("VTN", "COL1A1", "COL3A1", "LUM", "DCN", "PTGDS", "AMBP", "RGS5", "ACTA2", "TAGLN", "ABCC9", "KCNJ8") #3 GenMural, 6 VLMC, 2 Peri, 1 SMA
GeneLists[["NeuronsBroad"]] = c("SYT1", "SNAP25", "TUBB3","SYP", #Broad Neuronal
"SLC32A1", "DLX1", "DLX2", "DLX5", "GAD1", #GABAergic
"SLC17A6", "SLC17A8", #Glutaminergic
"SST", "TH", "TRH","BDNF", "OTP","PCSK1") #Pan regional neuropepetides or functional genes
GeneLists[["Zeisel_Vasculature"]] = toupper(c("Cldn5", "Adgrf5", "Emcn", "Vtn", "Cspg4", "Atp13a5", "Pth1r", "Kcnj8", "Abcc9", "Apln", "Cd82", "Chst1", "Tagln", "Pln", "Bmx", "Gkn3", "Igfbp2", "Dcn", "Lum", "Pdgfra", "Il33", "Ptgds", "Nnat", "Rspo3", "Nov", "Slc47a1", "Sox10", "Foxd3", "Aldh1a3", "Anxa11", "Slc18a2", "Klhl30", "Gfra3", "Cldn19", "Mpz", "Dhrs2", "Caecam10", "Cspg5", "Olig1", "Pcdh15", "Aldoc", "Npy", "Apod"))
GeneLists2 = GeneLists
for(GL in names(GeneLists2)){
GeneLists2[[GL]] = subset(GeneLists2[[GL]], GeneLists2[[GL]] %in% row.names(KaZhouAll))
}
Gene Lists: Neurons
GeneLists = list()
GeneLists[["NeuronsBroad"]] = c("SYT1", "SNAP25", "TUBB3","SYP", #Broad Neuronal
"SLC32A1", "DLX1", "DLX2", "DLX5", "GAD1", #GABAergic
"SLC17A6", "SLC17A8", #Glutaminergic
"SST", "TH", "TRH","BDNF", "OTP","PCSK1") #Pan regional neuropepetides or functional genes
GeneLists[["NeuronsARC"]] = c("NKX2-1", "KISS1", "GHRH", "POMC","NPY","AGRP", "HDC", "TBX3", "ISL1", "LEPR", "NPY1R", "CRABP1", "HMX2","GSX1") #HDC+ plus arcuate markers is the tuberomammullary terminal
GeneLists[["NeuronsPVH"]] = c("SIM1", "POU3F2", "NHLH2", "MC4R", "OXT", "AVP", "CRH", "GAL")
GeneLists[["NeuronsVMH"]] = c("GABRA5", "NPTX2", "FEZF1", "NR5A1")
GeneLists[["NeuronsMN"]] = c("PITX2", "FOXP1", "FOXP2", "FOXB1", "SOX14", "FEZF2", "LHX1")
GeneLists[["NeuronsLH"]] = c("HCRT", "CARTPT", "PMCH", "LHX2", "LHX9", "PDYN")
GeneLists[["NeuronsSCN"]] = c("RGS16","LHX1", "GRP", "VIP", "CCK", "VAX1", "SIX3", "SIX6")
GeneLists[["NeuronsSMN"]] = c("BARHL1", "FOXA1", "LMX1A", "IRX3", "IRX5")
GeneLists[["NeuronsDMH"]] = c("PPP1R17", "NKX2-2", "LHX8")
GeneLists[["NeuronsPO"]] = c("NR2F1", "NR2F2", "HMX3", "RELN")
CompileNeurons = unique(c(GeneLists[["NeuronsBroad"]], GeneLists[["NeuronsARC"]], GeneLists[["NeuronsPVH"]], GeneLists[["NeuronsVMH"]], GeneLists[["NeuronsMN"]], GeneLists[["NeuronsLH"]], GeneLists[["NeuronsSCN"]], GeneLists[["NeuronsSMN"]], GeneLists[["NeuronsDMH"]], GeneLists[["NeuronsPO"]]))
GeneLists[["Campbell_ARC"]] =unique(toupper(c("Hdc", "Gm8773", "Tac1", "Th", "Sst", "Sst", "Nts", "Nfix", "Htr2c", "Oxt", "Arx", "Nr5a2", "Th", "Slc6a3", "Th", "Nfib", "Ghrh", "Trh", "Cxcl12", "Agrp", "Sst", "Agrp", "Gm8773", "Pomc", "Ttr", "Pomc", "Anxa2", "Rgs16", "Vip", "Rgs16", "Dlx1", "Rgs16", "Nmu", "Gpr50", "Kiss1", "Tac2", "Pomc", "Glipr1", "Tmem215", "Sst", "Unc13c", "Sst", "Pthlh", "Trh", "Lef1", "Htr3b", "Tbx19", "Qrfp", "Nr5a1", "Bdnf", "Nr5a1", "Nfib", "Slc17a6", "Fam19a2", "Slc17a6", "Trhr")))
GeneLists[["Kim_VMH"]] =unique(toupper(c("Lhx1", "Slc18a3", "Lhx9", "Clic4", "Tbx3", "Tac2", "Anxa2", "Slc14a1", "Slc32a1", "Nup62cl", "Irx5", "Slc32a1", "Nfib", "Prdm13", "Car4", "Nr5a1", "Foxp2", "Prdm13", "Foxp2", "C1ql2", "Foxp2", "Tsix", "Esr1", "Gabrg3", "Esr1", "Twist2", "Esr1", "Insm2", "Samd9l", "Rorb", "Nmur2", "Smoc2", "Ngf", "Ctxn3", "Nup62cl", "Satb2", "Calcrl", "Six3", "Dlk1", "ll1rapl2", "F2rl2", "Nts", "Maob", "Adarb2", "Egflam", "Scgn", "Npffr2", "Glipr1", "Tcf712", "Ctxn3", "Car8", "Rorb")))
GeneLists[["Moffitt_PO"]] =unique(toupper(c("Tac1", "Fezf1", "Cartpt", "Isl1", "Trh", "Angpt1", "Adcyap1", "Nkx2-1", "Trp73", "Reln", "C1ql1", "Cck", "Ebf3", "Tcf7l2","Meis2", "Shox2", "Foxp2", "Ghrh", "C1ql1", "Ucn3", "Brs3", "Th", "Crh", "Rxpf3", "Gal", "Etv1", "Rxfp1", "Slc17a6", "Nts", "Slc17a8", "Slc32a1", "Gsc", "Pdyn", "Mylk", "Pou3f3", "Nms", "Amigo2", "Avp", "Cck", "Th", "Nos1", "Tac2", "Moxd1", "Sst", "Pmaip1", "Nmu", "Prok2", "Six6", "Igsf1", "Vip", "Calca", "Npy", "Bdnf", "Chrm2", "Kiss1", "Penk", "Pthlh", "Chat", "Cxcl14")))
GeneLists[["Mickelsen_LH"]] =unique(toupper(c("Pmch", "Gad1", "Nrgn", "Gda", "Zic1", "Tac1", "Pitx2", "Ebf3", "Otp", "Hcrt", "Tcf4", "Trh", "Cbln2", "Synpr", "Grp", "Cck", "Calca", "Col27a1", "Syt2", "Meis2", "Gpr101", "Sst", "Gal", "Dlk1", "Npy", "Npw", "Nts", "Cartpt", "Calb2", "Col25a1", "Tac2", "Serpini1", "Lhx6", "Calb1", "Th", "Slc18a2", "Atp1a2", "Cbln4")))
GeneLists[["Kim_WholeHypo"]] =unique(toupper(c("Oxt", "Avp", "Cartpt", "Isl1", "Meis2", "Gal", "Pax6", "Cck", "Hdc", "Lhx8", "Ghrh", "Cited1", "Six6", "Hmx2", "Agrp", "Npy", "Pomc", "Tac1", "Sst", "Onecut2", "Calb2", "Pnoc", "Foxp2", "Nr2f2", "Zfhx3", "Calb1", "Foxb1", "Lhx1", "Penk", "Hcrt", "Lhx9", "Nts", "Lhx2", "Gnrh", "Kiss1", "Meis3", "Pbx1", "Pbx3", "Six3", "Th", "Vip", "Chchd10", "Fezf1", "Grp", "Pmch", "Nr4a2", "Foxp1", "Lhx5", "Lmx1a", "Nhlh2", "Crh", "Arx", "Crh", "Otp")))
GeneLists[["Mickelsen_VPH"]] =unique(toupper(c("Tac2", "Pvalb", "Fgf1", "Cabp7", "Foxb1", "Cck", "Nts", "Col25a1", "Gpr83", "Spock3", "Slc24a2", "Nos1", "Calb1", "Cxcl14", "Synpr", "Stxbp6", "Tcf4", "Nr4a2", "Ebf3", "Slc6a1", "Tac1", "Foxp2", "Glra3", "Htr2c", "Hdc", "Slc18a2", "Wif1", "Prph", "Nrxn3", "Thrb", "Rxrg", "Unc13c", "Pdyn", "Kiss1", "Esr1", "Agrp", "Npy", "Otp", "Dlk1", "Six3", "Sst", "Pthlh", "Ptk2b", "Nnat", "Epha5", "Calcr", "Cplx1", "Hspa41", "Kcnc1", "Gabre", "Gad2", "Asb4")))
GeneLists[["Wen_Morris_SCN"]] =unique(toupper(c("Avp", "Nms", "Cck", "Grp", "Ppp1r17", "Gem", "Vip", "C1ql3", "Synpr", "Prok2", "Cck", "Prokr2", "Mef2c", "Peg10", "Gadd45a", "Hspa1a", "Vipr2", "Lbh", "Rasd1", "Igfbp5", "Ptp4a1", "Aip", "Pkib", "Chodl", "Sncb", "Nrxn3", "Alcam")))
GeneLists[["Affinati_VMH"]] =unique(toupper(c("Dlk1", "Drd3", "Pdgfd", "Hnf4g", "Col2a1", "Ivl", "Cdh20", "Col12a1", "Pdzrn3", "Bmp4", "Smoc2", "Robo3", "Esr1", "Dkk1", "Gpr174", "Fli1", "Nfia", "Duox2", "Eya1", "Eya4", "Nfib", "Adamts19", "Slc17a8", "Tacr1", "Exph5", "Cntnap3", "Olfr920", "Col5a2", "Atf7ip2", "Itih5", "Foxp2", "Lpar3", "Aass", "Col24a1", "Npffr2", "Dppa5a", "Hgt", "Adgr94", "Igf1", "Fezf1", "Nmu", "Rorb", "Efemp1", "Lepr", "Bmp15", "Zfp968", "Dlx60s1", "Parp4", "Acp7", "Cobil1", "Slc38a11","Glis3", "Crhr2", "St18", "Unc45b", "Fam83b", "Tll2", "Tll6", "Pantr1", "Acvr1c", "Barx2", "Zar1", "Adamts9", "Slc22a3", "Fign", "Tcf7l2", "Ankrd33b")))
GeneLists[["Zhou_WholeHypo"]] =unique(c("ONECUT1", "ONECUT2", "OTP", "SIM1", "SIX3", "DLX1", "DLX2", "DLX5", "DLX6", "ISL1", "MEIS2", "SST", "DLK1", "ASCL1", "TBX3", "ACHE", "FEZF1", "NPTX2", "GHRH", "GSX1", "PCP4", "AGRP", "NPY", "NPY1R", "POMC", "CBLN1", "KISS1", "TAC3", "PDYN", "HCRT", "CRABP1", "NHLH1", "CRABP2", "IRX1", "IRX4", "ADCYAP1", "SSTR2", "HDC", "SLC18A2", "GULP1", "CBLN4", "LHX6", "ARX", "CALB2","PITX2", "LHX5", "SIM1", "CALB2", "BARHL1", "BARHL2", "IRX2", "IRX3", "IRX5", "NEUROD2", "TBR1", "EOMES", "EMX2", "SP9", "GBX2", "TCF7L2", "GATA2", "GATA3"))
GeneLists[["Hypomap_ARC_PVH"]] =unique(toupper(c("Serpina3n", "Npy", "Agrp", "Acvr1c", "Npy", "Agrp", "Vcan", "Col6a1", "Il1rapl2", "Ptprk", "Npy", "Sst", "Il1rapl2", "Otp", "Sst", "Gpr101", "Otp", "Sst", "Crabp1", "Sytl4", "Tbx3", "Pou6f2", "Sytl4", "Tbx3", "Lepr", "Agrp", "Gpc3", "Bace2", "Epha3", "Il1rapl2", "Trh", "Nkx2-4", "Gpr50", "Nts", "Anxa2", "Pomc", "Glipr1", "Pomc", "Ttr", "Pomc", "Grp", "Ppp1r17", "Npvf", "Rfx4", "Qrfp", "Rfx4", "Nfix", "Satb2", "Slc6a3", "Satb2", "Nr5a2", "Satb2", "Nts", "Sst", "Pou3f1", "Sst", "Vgll3", "Tbx3", "Prdm12", "Tcf4", "Ghrh", "Lpar1", "Tac2",
"Shox2", "C2cd4b", "Fam122b", "Vipr2", "Igfbpl1", "Cbln2", "Trh", "Gsc", "Sncg", "Ebf1", "Ucn3", "Crh", "Tent5a", "Ebf3", "Caprin2", "Oxt")))
GeneLists[["Hypomap_MN_PO"]] =unique(toupper(c("Reln", "Lef1", "Hdc", "Pde11a", "Foxb1", "Ebf3",
"Ebf3", "Lmx1a", "Rxfp1", "Pitx2", "Slc6a3", "Nts", "Irx5", "Rxfp1", "Nts", "Myo5b", "Postn", "Nid1", "Postn", "Col19a1", "Fign", "Eya4", "Fign", "Nfia", "Nxph4", "Epha8", "Prkch",
"Igfbp4", "Lhx6", "Sfrp1", "Sox6", "Lhx8", "Slc17a8", "Ppp1r1b", "Bcl11b", "Meis2", "Ccn1", "Meis2", "Adora2a", "Ppp1r1b", "Tac1", "Meis2", "Cxcl14", "Sln", "Nfix", "Otx2", "Moxd1", "Dlx1", "Lhx6", "Crh", "Zeb2", "Lhx8", "Hmx2", "Gal", "Nts", "Dlx1", "Ucn3", "Meis2", "Samd3", "Eomes", "Lhx9", "Nfix", "Thrsp")))
GeneLists[["Hypomap_DMH_LH_AH"]] =unique(toupper(c("Zfp804b", "Hmcn1", "Tcf7l2", "Vcan","Tac1", "Prdm13", "Tcf4", "Crh", "Lhx9",
"Adgrf5", "Nxph4", "", "Alx1", "Pmfbp1", "Onecut2", "", "Npsr1", "Tmem114", "Lef1", "Fign", "Gal", "Crh", "Nts", "Piezo2", "Lhx6", "", "Crhbp", "Dlx1", "Ebf2", "Trh", "Lhx6", "Sox6", "Lhx8", "Syt2", "Trh", "Meis2", "Nts", "Ebf1", "Nkx2-4", "Otx1", "Gng8", "Samd3", "Tbr1", "Ndnf", "Meis2", "Samd3", "Ntng2", "Lef1", "Cped1", "Hcrt", "Rfx4", "Stk26", "Nxph4", "Pmch", "Pou6f2",
"Slc6a3", "Onecut2", "Nkd1", "Cd24a", "Lhx1", "Gal", "Hmcn1", "Mc4r", "Fezf1")))
GeneLists[["Hypomap_Peri_ZI"]] =unique(toupper(c("Cd79a", "Fam122b", "Vipr2", "Sst", "Tent5a", "Sparc", "Npy", "Sst", "Tbx19", "Il1rapl2", "Col11a1", "Sox14", "Lef1", "Myo5b", "Npsr1", "Barhl2", "Lef1", "Pbx3", "Lhx6",
"Pvalb", "Shisal2b", "Onecut3", "Onecut2", "Foxp2", "Tmem114", "Lef1", "Meis2")))
GeneLists[["Hypomap_SCN_VMH"]] =unique(toupper(c("Vip", "Vipr2", "Chodl", "Igfbp4", "Cck", "Nms", "Fam122b", "Prok2", "Prr5l", "Cdkn1c", "Lhx1", "Grp", "Cd24a", "Rrad",
"Gpr149", "Fezf1", "Col24a1", "Gpr88", "Arhgap36", "Cenpf", "Egflam", "Sox14", "Nkx2-2", "Satb2", "Cd40", "Nts", "Gldn", "Cbln4", "Necab1", "Esr1", "Iigp1", "Smim3", "Rai14", "Hmcn2", "Gpr149", "Slit3", "Nr5a1", "Tcf7l2", "Hmcn2", "Lamp5", "Bcl11b", "Nkd2", "Nr5a1", "H2-Q2", "Tac1", "Fezf1", "Robo3", "Sparc", "Nr5a1", "C1ql2", "Nr5a2", "Nfia", "Tcf4", "Adcyap1", "Tac1", "Lef1")))
Gene Lists: Betul (Endo/Epeny/Tany)
GeneLists = list()
GeneLists[["EpendyTanyList"]] = c("FOXJ1", "RAX", "ADM", "EMX2", "LHX2", "TMEM212", "CRYM", "CCDC153", "RFX2", "RFX3", "RFX4", "NPAS3", "COL23A1", "CD59", "SLC17A8", "VCAN", "FRZB", "PENK", "COL25A1", "CACNA2D2", "STOML3", "TMEM212", "SLC7A11", "PCP4L1", "PLTP", "TGFB2", "NR2E1", "EPHB1", "P3H2", "NELL2", "FGF10", "RGCC", "GRIA2", "RLBP1", "SIX6", "SCN7A", "MEST", "IGFBP5", "RGS7BP")
GeneLists[["EpendyTany_CampbellHeatmap"]] = toupper(c("Stoml3", "Ccdc153", "Tmem212", "Pcp4l1", "Tm4sf1", "Pltp", "Slc1a2", "Tgfb2", "Agt", "Gfap", "Slc7a11", "Vcan", "Nr2e1", "Ephb1", "P3h2", "Nell2", "Gria2", "Rlbp1", "Frzb", "Penk", "Six6", "Fgf10", "Scn7a", "Fndc3c1", "Mest", "Col25a1", "Igfbp5", "Rgcc", "Adm", "Rgs7bp", "Ctgf"))
GeneLists[["EndoList"]] = c("SLCO1C1", "SLC38A5", "MYH11", "MRC1", "CLDN5", "ITM2A", "FLT1")
GeneLists[["VLMC"]] = c("COL1A1", "COL3A1", "LUM", # Campbell
"PTGDS", # Zhou
"DCN"
)
## Mural cells are the vascular smooth muscle cells (vSMCs), and pericytes,
## of the microcirculation.
GeneLists[["Mural"]] = c("MUSTN1", "SLCO1C1", "CSPG4", # Campbell
"PDGFRB", "NDUFA4L2", # Zhou_Fig1_MarkerGenes
"MYL9" # Valandewijck ** Pan-mural cell marker
)
GeneLists[["Pericytes"]] = c("KCNJ8", "PDGFRB", "CSPG4", "ANPEP", # Valandewijck_Fig1
"RGS5", "CD248", "ABCC9", "VTN", "S1PR3" # Valandewijck_Fig1
)
GeneLists[["vSMCs"]] = c("PDLIM3", "ACTA2", "TAGLN", "MYH11", # Valandewijck_Fig1
"MYL9", "MYLK", "SNCG", "CNN1", "PLN" # Valandewijck_Fig1
)
## ECs: Endothelial Cells
GeneLists[["Endothelium"]] = unique(c("CLDN5", # Zhou, Valandewijck_Fig1, Chen: Endo
"FLT1", "SLC38A5", # Zhou_Fig1_MarkerGenes ** "SCL38A5"-Chen: Endo1
"SLCO1C1", # Campbell
"MFSD2A", # Valandewijck ** Endothelial-specific transcript
"MYH11",
"MRC1", # Chen: Endo2
"ITM2A",
"PECAM1", "CDH5", # Kalucka
"BCAM", "ESAM", "LY6C1", "LY6A", # Kalucka_FigS1
"CD36", "SOX17", "NRP1", # Kalucka_FigS1
"PGLYRP1", # Kalucka
"FOXQ1", "FOXF2", # Kalucka_Fig2
"PECAM1", "KDR", "FLT1", "TIE1", # Valandewijck_Fig1
"TEK", "ICAM2", "PODXL", "PTPRB" # Valandewijck_Fig1
))
GeneLists[["LargeArtery"]] = unique(c("MGP", "CYTL1", "FBLN5", "BMX", # Kalucka_FigS3
"CLU", "ELN", "BGN", "IGFBP4", # Kalucka_TableS2
"CFH", "LMCD1", "BPGM" # Kalucka_TableS2
))
GeneLists[["Artery"]] = unique(c("BMX", # Zeisel
"GKN3", "HEY1", # Kalucka, Zeisel
"EDN3", # Kalucka_TableS2, Zeisel_FigS3
"CLDN5", "ADGRF5", "VTN", "CSPG4", "ATP13A5", # Zeisel_Fig5
"PTH1R", "TAGLN", "IGFBP2", "DCN", "LUM", # Zeisel_Fig5
"PDGFRA", "IL33", # Zeisel_Fig5
"ATF3", "CRIP1", "FBLN2", "GJA4", "MECOM", # Kalucka_TableS5
"SAT1", "SEMA3G", "SOX17", # Kalucka_TableS5
"EGFL8", "VEGFC", "HRCT1", "CDKN1C", # Kalucka_TableS2
"EPS8L2", "ALPL", "MGST1", # Kalucka_TableS2
"NR4A2", "JUNB", "GATA6", "STAT4", "SETBP1", # Valandewijck_Ext6
"NFATC4", "ATF3", "MYBL2", "CSRNP1", "NR4A1", # Valandewijck_Ext6
"IRF6", "ZKSCAN4", "FOS", "GATA2", "MIER2", # Valandewijck_Ext6
"HSF4", "EGR1", "ZBP1", "BHLHE41", "TRF", # Valandewijck_Ext6
"SLC36A1", "MTTP", "SLC12A5", "SCNN1A", # Valandewijck_Ext6
"ATP2A3", "SLC48A1", "ABCA5", "GRIA1", # Valandewijck_Ext6
"SLC6A8", "ATP11A", "PLSCR2", "SLC12A8", # Valandewijck_Ext6
"SLC45A4", "GJA5", "KCNN4", "SNAP25", "FXYD4" # Valandewijck_Ext6
))
# Arteriolar ECs express both arterial and capillary markers
# (capillary-arterial)
GeneLists[["Arteriolar"]] = unique(c("GLUL", "SLC26A10", # Kalucka_FigS3
"TGFB2", # Kalucka_FigS5
"RAD54B", "TUBA1A", "STMN1", "AKAP12", # Kalucka_TableS2
"KLF6", "DUSP1", "ZFP771" # Kalucka_TableS2
))
GeneLists[["Capillary"]] = unique(c("MEOX1", # Zeisel
"CLDN5", "ADGRF5", "EMCN", # Zeisel_Fig5
"APLN", "CD82", "CHST1", "APOD", # Zeisel_Fig5
"CXCL12", "SPOCK2", # Kalucka_FigS3
"MFSD2A", "RGCC", # Kalucka_FigS5
"ABLIM1", "BSG", "SLCO1A4", "SLCO1C1", # Kalucka_TableS5
"SPOCK2", "TSC22D1", # Kalucka_TableS5
"CD300LG", "SGK1", "SPARC", # Kalucka_TableS5
"GM9946", "LRRN3", "GSTM7", "PALM", # Kalucka_TableS2
"TBX3", "PRDM16", "RASGRP2", "CD83", # Kalucka_TableS2
"ANGPT2", # Valandewijck_Fig2
"PRDM11", "PLEK", "HEYL", "MEOX1", "ZFP382", # Valandewijck_Ext6
"ZFP457", "SALL1", "TBX2", "KLF15", "LBX2", # Valandewijck_Ext6
"GLI3", "HNF1A", "HIF3A", "TUB", "FOXM1", # Valandewijck_Ext6
"ZBTB41", "ETV5", "PRDM1", "ZFHX2", "ZFP334" # Valandewijck_Ext6
))
# Venular ECs express both venous and capillary markers
# (capillary-venous)
GeneLists[["Venular"]] = unique(c("CAR4", "ITM2A", # Kalucka_FigS3
"TFRC", "CAR4", # Kalucka_FigS5
"GATM", "HMCN1", "SLC7A1", "NKD1", # Kalucka_TableS2
"HSPA1A", "SMOX", "SLC31A1", "SLC39A8" # Kalucka_TableS2
))
GeneLists[["Venous"]] = unique(c("SLC38A5", # Zeisel
"CLDN5", "ADGRF5", "EMCN", # Zeisel_Fig5
"APOD", "CTSC", # Kalucka_TableS5
"IL6ST", "APOE", "CTLA2A", # Kalucka_TableS5
"NR2F2", # Valandewijck_Fig2
"ZC3H12B", "THAP6", "TCF15", "KLF10", "AR", # Valandewijck_Ext6
"CARHSP1", "ZFP691", "ZFAT", "SOX12", "BCL11A", # Valandewijck_Ext6
"ZFP341", "DACH2", "MBNL3", "CREB3L4", "TBX1", # Valandewijck_Ext6
"LCN2", "CACNA1B", "PTGDS", "KCNB1" # Valandewijck_Ext6
))
GeneLists[["LargeVein"]] = c("TMSB10", "ICAM1", "VWF", "ACKR1", # Kalucka_FigS3
"SLC38A5", "LCN2", # Kalucka_FigS5
"VCAM1", "TMEM252", "CTSC", "TGM2", "ACKR1" # Kalucka_TableS2
)
GeneLists[["Choroid_ECs"]] = c("PLVAP", "KDR", "PLPP3", # Kalucka_FigS3
"PLPP1", "CD24A", "NRP1", "ESM1", # Kalucka
"SOCS3", "RGCC", "IGFBP3", "EHD4" # Kalucka_TableS2
)
# Interferon-activated ECs
GeneLists[["Interferon_ECs"]] = c("ISG15", "IFIT1", "IFIT3", "IFIT3B", # Kalucka_FigS3
"PGLYRP1", "SLC7A5", "SPARCL1", # Kalucka_TableS2
"SLC3A2", "SLC38A3", "USP18" # Kalucka_TableS2
)
# Glia
GeneLists[["EpendymalCells"]] = unique(c("SOX2", # Campbell
"VIM", "NES", # Langlet, Chen ** NES&VIM (radial glia markers) are highly expressed in tany&ependy
"PRDX6", "MT1", "MT2", "DLK1" # Campbell_S2d
))
GeneLists[["Ependy"]] = unique(c("CCDC153", # Campbell_Fig1, Chen, Zhou_Fig1_MarkerGenes
"CCDC74A", "CCDC74B", "LRTOMT", # Zhou_MarkerGenes
"FOXJ1",
"RFX2", "RFX3", "RFX4",
"STOML3", "TMEM212", "PCP4L1", "TM4SF1", "PLTP",# Campbell_Fig2
"CDHR4", "CALB1", "ITIH5", # Campbell_S2d
"HDC" # Chen_Fig4
))
GeneLists[["EpyII"]] = c("FLT1", "RSPO3", "RLBP1", "LRRN1" # Campbell_S2d
)
# Tanycytes: blood-brain traffic controllers, metabolic modulators, neural stem/progenitor cells
GeneLists[["Tanycytes"]] = c("RAX", "ADM", "CRYM", # Campbell_Fig1
"FRZB",
"COL23A1", "SLC16A2", "LHX2", "PTN", # Langlet, Chen ** SLC16A2 & LHX2 have been linked to tanycyte development
"PPP1R1B", "DIO2", "PRDX6", # Langlet
"GCK", "DIO3", "OATP1C1", "GPR50", "NMUR2", # Langlet_Table1
"ALDH1A1", "TTR", "CRBP1", "STRA6", # Langlet_Table1
"CRABP2", "CNTF", # Langlet_Table1
"FGF10" # Steuernagel
)
# Alpha Tanycytes: gene markers are more similar to non-tanycyte ependymal cells than beta tanycytes
# modulation of neuronal activity
GeneLists[["ALPHA"]] = c("SLC1A3", "SLC16A1", "SLC16A4", # Langlet ** Glutamate and lactate transport - neuronal modulation
"GJA1", "FGF18", "PRSS56", "CD59A", "CRYM", # Langlet
"VCAN"
)
GeneLists[["Alpha1"]] = c("SLC1A2", "TGFB2", "AGT", "GFAP", # Campbell_Fig2
"SLC7A11", # Campbell_Fig2
"RSPO3", # Campbell_S2d
"SLC17A8", # Chen_Fig4
"MAFB", # Yoo_Fig3
"OCLN" # Langlet_Table1
)
GeneLists[["alpha1.1"]] = c("RSPO3", "TMEM212", "ITIH5", "FLT1" # Campbell_S2d
)
GeneLists[["alpha1.2"]] = c("RSPO3", "SLC17A8", "LYZ2" # Campbell_S2d
)
GeneLists[["Alpha2"]] = c("VCAN", "NR2E1", "EPHB1", "P3H2", "NELL2", # Campbell_Fig2
"PDZPH1", # Campbell_S2d
"FRZB", "PENK" # Langlet
)
# Beta Tanycytes: component of blood-brain interface
# control the access of nutrients and hormones to brain & secretion of
# neuropeptides into hypophysial vascular system in the ME
GeneLists[["BETA"]] = c("CLDN1", "VEGF", "CDH2", "CAVL", "SLC2A1", # Langlet ** SLC2A1 is expressed more in beta1
"SOX2", "FGF10", "BLBP", "MSI1", # Langlet ** Neural stem cell markers - Stem/Progenitor cell fxn
"FGFR1", "CNTFR", # Langlet ** Growth factor rec. - Stem/Progenitor cell fxn.
"COL25A1", "CACNA2D2", "ADM", # Langlet
"CLDN10" # Campbell_S2d
)
GeneLists[["Beta1"]] = unique(c("GRIA2", "RLBP1", "FRZB", "PENK", # Campbell_Fig2
"SIX6", "FGF10", # Campbell_Fig2
"LRRN1", # Campbell_S2d
"FRZB", "PENK" # Langlet
))
GeneLists[["Beta2"]] = unique(c("SCN7A", "FNDC3C1", "MEST", "ADM", # Campbell_Fig2
"COL25A1", "IGFBP5", "RGCC", "RGS7BP", # Campbell_Fig2
"CTGF", # Campbell_Fig2
"TRHDE" # Langlet_Table1
))
GeneLists[["beta2.1"]] = c("FRZB", "SIM1" # Campbell_S2d
)
GeneLists[["beta2.2"]] = c("CYSLTR1", "LRRTM3" # Campbell_S2d
)
for(GL in names(GeneLists)){
GeneLists[[GL]] = subset(GeneLists[[GL]], GeneLists[[GL]] %in% row.names(KaZhouAll))
}
Read in ALL data and iterate UMAPs
KaZhouAll = readRDS("~/Dropbox/LabMac/KaZhouAll_mt10_integrated.rds")
CleanedClusters_Figure1 = read.csv("~/Library/CloudStorage/Box-Box/HG2553 Main Folder/Science Advances/CleanedClusters_Figure1_19DEC22.csv", row.names =1)
KaZhouAll@meta.data$Timepoint = gsub("_.*", "", gsub("T", "", gsub("CS13", "GW6", gsub("CS14", "GW7", gsub("CS15", "GW7", gsub("CS22", "GW10", KaZhouAll@meta.data$sample))))))
KaZhouAll@meta.data$Timepoint2 = ifelse(KaZhouAll@meta.data$Study == "Zhou", paste(KaZhouAll@meta.data$Timepoint, "[Zhou]"), KaZhouAll@meta.data$Timepoint)
MainClusters = CleanedClusters_Figure1 %>% dplyr::select("Pop")
row.names(MainClusters) = CleanedClusters_Figure1$Row.names
MainClusters$Pop = gsub(" \\[.*", "", MainClusters$Pop)
KaZhouAll = AddMetaData(KaZhouAll, MainClusters, "MainClusters")
Idents(KaZhouAll) = "MainClusters"
GenesToPlot = GeneLists[["OligList"]] #GeneLists[["AstroMicro"]] #c(GeneLists[["Zhou_MainMarkers"]], GeneLists[["Zhou_MainMarkersV2"]])
#"Neuroepithelial" , "Ependymal", "RadialGlia", "Dividing", "Endothelial", "Tanycytes", "Microglia", "VLMC", "IntProgen",
for(Pops in c("Oligodendrocytes")){
SubsetSeu = subset(KaZhouAll, idents = Pops)
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = FindVariableFeatures(SubsetSeu)
SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
#VF_new = SubsetSeu@assays$integrated@var.features
DPlist2 = list()
DPlist = list()
FPlist = list()
Filename = paste(Pops, "29MAR23", sep="")
for(y in seq(10,50,10)){
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = RunPCA(SubsetSeu, npcs = y)
for(z in seq(10,y,10)){
for(s in c(2,5,10)){
SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s)
DefaultAssay(SubsetSeu) = "RNA"
FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, GenesToPlot, reduction="umap")
DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "Timepoint2") + labs(title = paste("PCA", y, "_dims", z, "_spread", s))
DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "Timepoint2", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s))
}}}
pdf(paste("FETAL_HYPO_155K_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=77/1.3)
print(FPlist)
dev.off()
pdf(paste("FETAL_HYPO_155K_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5)
print(DPlist)
dev.off()
pdf(paste("FETAL_HYPO_155K_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5)
print(DPlist2)
dev.off()
}
for(Pops in c("Oligodendrocytes")){
SubsetSeu = subset(KaZhouAll, idents = Pops)
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = FindVariableFeatures(SubsetSeu)
SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DPlist2 = list()
DPlist = list()
FPlist = list()
Filename = paste(Pops, "29MAR23_Harmony", sep="")
for(y in seq(10,50,10)){
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = RunPCA(SubsetSeu, npcs = y)
SubsetSeu <- RunHarmony(SubsetSeu, group.by.vars = "Timepoint2")
for(z in seq(10,y,10)){
for(s in c(2,5,10)){
SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s, reduction = "harmony")
DefaultAssay(SubsetSeu) = "RNA"
FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, GenesToPlot, reduction="umap")
DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "Timepoint2") + labs(title = paste("PCA", y, "_dims", z, "_spread", s))
DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "Timepoint2", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s))
}}}
pdf(paste("FETAL_HYPO_155K_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=77/1.3)
print(FPlist)
dev.off()
pdf(paste("FETAL_HYPO_155K_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5)
print(DPlist)
dev.off()
pdf(paste("FETAL_HYPO_155K_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5)
print(DPlist2)
dev.off()
}
-
Non Neuronal Subclustering
Microglia
GeneLists[["Olah_Microglia"]] = c("PTPRC", "ITGAM", "AIF1", "C1QA", "CTSS", "CD14", "CSF3R", "ARGLU1", "FAM46A", "IFIT3", "ISG15", "MRC1", "TNF", "CD83", "EGR2", "TNFSF18", "CCL8", "TFRC", "FCGBP", "GPR84", "PONA", "CDC20", "BIRC5") #Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease
GeneLists[["Tansley_Microglia"]] = c("TMEM119", "FCRTS", "P2RY12", "CX3CR1", "TREM2", "C1QA", "GM3336", "SPP1", "IFIT3", 'MKI67', "MCM6", "CLDN5", "H2-EB1") #Single-cell RNA sequencing reveals time- and sex-specific responses of mouse spinal cord microglia to peripheral nerve injury and links ApoE to chronic pain
Idents(KaZhouAll) = "MainClusters"
Microglia_Seu = subset(KaZhouAll, idents = "Microglia")
DefaultAssay(Microglia_Seu) = "integrated"
Microglia_Seu = FindVariableFeatures(Microglia_Seu)
Microglia_Seu = ScaleData(Microglia_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Microglia_Seu) = "integrated"
Microglia_Seu = RunPCA(Microglia_Seu, npcs = 20)
Microglia_Seu <- RunUMAP(Microglia_Seu, dims = 1:20, spread= 2)
DefaultAssay(Microglia_Seu) = "RNA"
#####
set.dim = c(20)
set.res = 1
set.kparam = c(30)
#ClusterFunc_All_RNA(Microglia_Seu)
DefaultAssay(Microglia_Seu) = "integrated"
Microglia_Seu <- FindNeighbors(Microglia_Seu, k.param=30, dims=1:20)
Microglia_Seu <- FindClusters(Microglia_Seu, resolution = 1)
DefaultAssay(Microglia_Seu) = "RNA"
#CheckInput = Microglia12Clean
#CheckUMAP(Microglia_Seu)
Microglia_UMAP = as.data.frame(Microglia_Seu@reductions$umap@cell.embeddings)
Microglia01 = subset(Microglia_Seu, idents = c(0))
Microglia01Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia01) & Microglia_UMAP$UMAP_1 > -2 & Microglia_UMAP$UMAP_1 < 8 & Microglia_UMAP$UMAP_2 > 0)
Microglia02 = subset(Microglia_Seu, idents = 1)
Microglia02Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia02) & Microglia_UMAP$UMAP_1 < 2 & Microglia_UMAP$UMAP_2 > 0.5)
Microglia03 = subset(Microglia_Seu, idents = c(2,5))
Microglia03Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia03) & Microglia_UMAP$UMAP_1 > 2 & Microglia_UMAP$UMAP_1 < 7 & Microglia_UMAP$UMAP_2 > -5 & Microglia_UMAP$UMAP_2 < 4.5 | Microglia_UMAP$UMAP_1 > -5 & Microglia_UMAP$UMAP_1 < 2 & Microglia_UMAP$UMAP_2 > -2.5 & Microglia_UMAP$UMAP_2 < 1.5)
Microglia04 = subset(Microglia_Seu, idents = 3)
Microglia04Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia04)& Microglia_UMAP$UMAP_1 >3)
Microglia05 = subset(Microglia_Seu, idents = c(4,8))
Microglia05Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia05) & Microglia_UMAP$UMAP_1 > -5 & Microglia_UMAP$UMAP_2 < -10)
Microglia06 = subset(Microglia_Seu, idents = 6)
Microglia06Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia06) & Microglia_UMAP$UMAP_1 > -4 & Microglia_UMAP$UMAP_1 < 2 & Microglia_UMAP$UMAP_2 > -10)
Microglia07Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia06) & Microglia_UMAP$UMAP_1 > 8)
Microglia08 = subset(Microglia_Seu, idents = c(7))
Microglia08Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia08) & Microglia_UMAP$UMAP_1 < -10)
Microglia09 = subset(Microglia_Seu, idents = c(9, 10))
Microglia09Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia09) & Microglia_UMAP$UMAP_1 > -9.2 & Microglia_UMAP$UMAP_1 < -4 & Microglia_UMAP$UMAP_2 < -12)
Microglia10Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia09) & ! row.names(Microglia_UMAP) %in% row.names(Microglia09Clean) & Microglia_UMAP$UMAP_1 > -11 & Microglia_UMAP$UMAP_2 < -8)
Microglia11 = subset(Microglia_Seu, idents = 11)
Microglia11Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia11) & Microglia_UMAP$UMAP_1 > -10 & Microglia_UMAP$UMAP_1 < 0)
Microglia12 = subset(Microglia_Seu, idents = 12)
Microglia12Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia12) & Microglia_UMAP$UMAP_1 < -7)
Microglia13 = subset(Microglia_Seu, idents = 13)
Microglia13Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia13) & Microglia_UMAP$UMAP_1 < -8 & Microglia_UMAP$UMAP_2 > -10)
#CheckInput = Microglia03_Extras
#CheckUMAP(Microglia_Seu)
Microglia_REST = subset(Microglia_Seu, cells = c(row.names(Microglia01Clean), row.names(Microglia02Clean), row.names(Microglia03Clean), row.names(Microglia04Clean), row.names(Microglia05Clean), row.names(Microglia06Clean), row.names(Microglia07Clean), row.names(Microglia08Clean), row.names(Microglia09Clean), row.names(Microglia10Clean), row.names(Microglia11Clean), row.names(Microglia12Clean), row.names(Microglia13Clean)), invert=T)
#CheckInput = Microglia05_Extras
#CheckUMAP(Microglia_Seu)
Microglia01_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 1 & Microglia_UMAP$UMAP_2 > 3 | row.names(Microglia_UMAP) %in% row.names(Microglia01Clean))
Microglia02_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 < 1 & Microglia_UMAP$UMAP_1 > -6.2 & Microglia_UMAP$UMAP_2 > 0 | row.names(Microglia_UMAP) %in% row.names(Microglia02Clean))
Microglia03_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & ! row.names(Microglia_UMAP) %in% c(row.names(Microglia02_Extras), row.names(Microglia01_Extras)) & Microglia_UMAP$UMAP_1 > -4 & Microglia_UMAP$UMAP_1 < 8 & Microglia_UMAP$UMAP_2 < 4 & Microglia_UMAP$UMAP_2 > -3.2 | row.names(Microglia_UMAP) %in% row.names(Microglia03Clean))
Microglia03_Extras = subset(Microglia03_Extras, ! row.names(Microglia03_Extras) %in% c(row.names(Microglia02_Extras), row.names(Microglia01_Extras)))
Microglia04_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 3 & Microglia_UMAP$UMAP_2 < -2 & Microglia_UMAP$UMAP_2 > -13 & ! row.names(Microglia_UMAP) %in% row.names(Microglia03_Extras) | row.names(Microglia_UMAP) %in% row.names(Microglia04Clean))
Microglia05_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -5 & Microglia_UMAP$UMAP_1 < 3 & Microglia_UMAP$UMAP_2 < -9 | row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 4 & Microglia_UMAP$UMAP_2 < -12 | row.names(Microglia_UMAP) %in% row.names(Microglia05Clean))
Microglia06_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -4 & Microglia_UMAP$UMAP_1 < 3 & Microglia_UMAP$UMAP_2 > -10 & Microglia_UMAP$UMAP_2 < -3.2 | row.names(Microglia_UMAP) %in% row.names(Microglia06Clean))
Microglia07_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 8 & Microglia_UMAP$UMAP_2 < 2 | row.names(Microglia_UMAP) %in% row.names(Microglia07Clean))
Microglia08_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 < -10 & Microglia_UMAP$UMAP_2 > -1 | row.names(Microglia_UMAP) %in% row.names(Microglia08Clean))
Microglia09_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -9.2 & Microglia_UMAP$UMAP_1 < -4 & Microglia_UMAP$UMAP_2 < -12 | row.names(Microglia_UMAP) %in% row.names(Microglia09Clean))
Microglia10_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -11 & Microglia_UMAP$UMAP_1 < -5 & Microglia_UMAP$UMAP_2 < -8 & ! row.names(Microglia_UMAP) %in% row.names(Microglia09_Extras)| row.names(Microglia_UMAP) %in% row.names(Microglia10Clean))
Microglia11_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_2 > -8 & Microglia_UMAP$UMAP_2 < -2 & Microglia_UMAP$UMAP_1 > -8 & Microglia_UMAP$UMAP_1 < -3 & ! row.names(Microglia_UMAP) %in% row.names(Microglia06_Extras)| row.names(Microglia_UMAP) %in% row.names(Microglia11Clean))
Microglia12_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -10 & Microglia_UMAP$UMAP_1 < -6 & Microglia_UMAP$UMAP_2 > -2 & Microglia_UMAP$UMAP_2 < 1 | row.names(Microglia_UMAP) %in% row.names(Microglia12Clean))
Microglia13_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 < -8 & Microglia_UMAP$UMAP_2 > -10 & Microglia_UMAP$UMAP_2 < -5 | row.names(Microglia_UMAP) %in% row.names(Microglia13Clean))
Microglia_Assigns = GenerateMetaData_Barcodes(list("Microglia_01" = Microglia01_Extras, "Microglia_02" = Microglia02_Extras, "Microglia_03" = Microglia03_Extras, "Microglia_04" = Microglia04_Extras, "Microglia_05" = Microglia05_Extras, "Microglia_06" = Microglia06_Extras, "Microglia_07" = Microglia07_Extras, "Microglia_08" = Microglia08_Extras, "Microglia_09" = Microglia09_Extras, "Microglia_10" = Microglia10_Extras, "Microglia_11" = Microglia11_Extras, "Microglia_12" = Microglia12_Extras, "Microglia_13" = Microglia13_Extras))
#Microglia02_Extras_Pt2 = subset(Microglia_Seu, cells = Microglia_Assigns$Barcodes, invert=T)
#CheckInput = Microglia03_Extras
#CheckUMAP(Microglia_Seu)
Microglia_Assigns$Dups = duplicated(Microglia_Assigns$Barcodes) | duplicated(Microglia_Assigns$Barcodes, fromLast=T)
Microglia_Assigns_T = subset(Microglia_Assigns, Microglia_Assigns$Dups == T)
unique(Microglia_Assigns_T$Pop)
Microglia_Assigns = GenerateMetaData(list( "Microglia_01" = Microglia01_Extras, "Microglia_02" = Microglia02_Extras, "Microglia_03" = Microglia03_Extras, "Microglia_04" = Microglia04_Extras, "Microglia_05" = Microglia05_Extras, "Microglia_06" = Microglia06_Extras, "Microglia_07" = Microglia07_Extras, "Microglia_08" = Microglia08_Extras, "Microglia_09" = Microglia09_Extras, "Microglia_10" = Microglia10_Extras, "Microglia_11" = Microglia11_Extras, "Microglia_12" = Microglia12_Extras, "Microglia_13" = Microglia13_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
VLMC
Idents(KaZhouAll) = "MainClusters"
VLMC_Seu = subset(KaZhouAll, idents = "VLMC")
DefaultAssay(VLMC_Seu) = "integrated"
VLMC_Seu = FindVariableFeatures(VLMC_Seu)
VLMC_Seu = ScaleData(VLMC_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(VLMC_Seu) = "integrated"
VLMC_Seu = RunPCA(VLMC_Seu, npcs = 30)
VLMC_Seu <- RunHarmony(VLMC_Seu, group.by.vars = "Timepoint2")
VLMC_Seu <- RunUMAP(VLMC_Seu, dims = 1:30, spread= 5, reduction = "harmony")
DefaultAssay(VLMC_Seu) = "RNA"
#####
set.dim = c(30)
set.res = 1
set.kparam = c(30, 50, 100)
#ClusterFunc_All_RNA(VLMC_Seu)
DefaultAssay(VLMC_Seu) = "integrated"
VLMC_Seu <- FindNeighbors(VLMC_Seu, k.param=30, dims=1:30)
VLMC_Seu <- FindClusters(VLMC_Seu, resolution = 1)
DefaultAssay(VLMC_Seu) = "RNA"
#CheckInput = VLMC08Clean
#CheckUMAP(VLMC_Seu)
VLMC_UMAP = as.data.frame(VLMC_Seu@reductions$umap@cell.embeddings)
VLMC01 = subset(VLMC_Seu, idents = c(0))
VLMC01Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC01) & VLMC_UMAP$UMAP_1 < -23 & VLMC_UMAP$UMAP_2 > -15)
VLMC02 = subset(VLMC_Seu, idents = 4)
VLMC02Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC02) & VLMC_UMAP$UMAP_1 > -26 & VLMC_UMAP$UMAP_1 < -14 & VLMC_UMAP$UMAP_2 > -8)
VLMC03 = subset(VLMC_Seu, idents = c(9))
VLMC03Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC03) & VLMC_UMAP$UMAP_1 < -15)
VLMC04 = subset(VLMC_Seu, idents = 3)
VLMC04Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC04) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 0)
VLMC05 = subset(VLMC_Seu, idents = c(7))
VLMC05Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC05) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 > -25 & VLMC_UMAP$UMAP_2 < -5)
VLMC06 = subset(VLMC_Seu, idents = 11)
VLMC06Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC06) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 < -25)
VLMC07 = subset(VLMC_Seu, idents = c(7,11,3,9,4,0), invert=T)
VLMC07Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC07) & VLMC_UMAP$UMAP_1 > 0 & VLMC_UMAP$UMAP_2 > -10 | row.names(VLMC_UMAP) %in% colnames(VLMC07) & VLMC_UMAP$UMAP_1 > 8 )
#CheckInput = VLMC03_Extras
#CheckUMAP(VLMC_Seu)
VLMC_REST = subset(VLMC_Seu, cells = c(row.names(VLMC01Clean), row.names(VLMC02Clean), row.names(VLMC03Clean), row.names(VLMC04Clean), row.names(VLMC05Clean), row.names(VLMC06Clean), row.names(VLMC07Clean)), invert=T)
#CheckInput = VLMC07_Extras
#CheckUMAP(VLMC_Seu)
VLMC01_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 < -25 & VLMC_UMAP$UMAP_2 > -11 | row.names(VLMC_UMAP) %in% row.names(VLMC01Clean))
VLMC02_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -25 & VLMC_UMAP$UMAP_1 < -14 & VLMC_UMAP$UMAP_2 > -11 | row.names(VLMC_UMAP) %in% row.names(VLMC02Clean))
VLMC03_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & ! row.names(VLMC_UMAP) %in% c(row.names(VLMC02_Extras), row.names(VLMC01_Extras)) & VLMC_UMAP$UMAP_1 < -14 | row.names(VLMC_UMAP) %in% row.names(VLMC03Clean))
VLMC04_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 0 & VLMC_UMAP$UMAP_2 > 0 | row.names(VLMC_UMAP) %in% row.names(VLMC04Clean))
VLMC05_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 > -25 & VLMC_UMAP$UMAP_2 < -5 | row.names(VLMC_UMAP) %in% row.names(VLMC05Clean))
VLMC06_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 < -25| row.names(VLMC_UMAP) %in% row.names(VLMC06Clean))
VLMC07_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > 0 & VLMC_UMAP$UMAP_2 > -10 | row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > 8 | row.names(VLMC_UMAP) %in% row.names(VLMC07Clean))
VLMC_Assigns = GenerateMetaData_Barcodes(list("VLMC_01" = VLMC01_Extras, "VLMC_02" = VLMC02_Extras, "VLMC_03" = VLMC03_Extras, "VLMC_04" = VLMC04_Extras, "VLMC_05" = VLMC05_Extras, "VLMC_06" = VLMC06_Extras, "VLMC_07" = VLMC07_Extras))
#VLMC02_Extras_Pt2 = subset(VLMC_Seu, cells = VLMC_Assigns$Barcodes, invert=T)
#CheckInput = VLMC03_Extras
#CheckUMAP(VLMC_Seu)
VLMC_Assigns$Dups = duplicated(VLMC_Assigns$Barcodes) | duplicated(VLMC_Assigns$Barcodes, fromLast=T)
VLMC_Assigns_T = subset(VLMC_Assigns, VLMC_Assigns$Dups == T)
unique(VLMC_Assigns_T$Pop)
VLMC_Assigns = GenerateMetaData(list( "VLMC_01" = VLMC01_Extras, "VLMC_02" = VLMC02_Extras, "VLMC_03" = VLMC03_Extras, "VLMC_04" = VLMC04_Extras, "VLMC_05" = VLMC05_Extras, "VLMC_06" = VLMC06_Extras, "VLMC_07" = VLMC07_Extras))
#load("~/Hypothalamus_Subclustering_APR2023.RData")
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Pericytes
GeneLists[["Garcia_Pericytes"]] = c("ANXA2", "PDGFRB", "ANO3", "RERGL", "STAC", "APPBP2", "TERF2IP", "PCSK7", "GABRE", "KLHL29", "PGAP1") #Single-cell dissection of the human brain vasculature
Idents(KaZhouAll) = "MainClusters"
Pericytes_Seu = subset(KaZhouAll, idents = "Pericytes")
DefaultAssay(Pericytes_Seu) = "integrated"
Pericytes_Seu = FindVariableFeatures(Pericytes_Seu)
Pericytes_Seu = ScaleData(Pericytes_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Pericytes_Seu) = "integrated"
Pericytes_Seu = RunPCA(Pericytes_Seu, npcs = 20)
Pericytes_Seu <- RunHarmony(Pericytes_Seu, group.by.vars = "Timepoint2")
Pericytes_Seu <- RunUMAP(Pericytes_Seu, dims = 1:20, spread= 2, reduction = "harmony")
DefaultAssay(Pericytes_Seu) = "RNA"
#####
set.dim = c(20)
set.res = 1
set.kparam = c(80)
#ClusterFunc_All_RNA(Pericytes_Seu)
DefaultAssay(Pericytes_Seu) = "integrated"
Pericytes_Seu <- FindNeighbors(Pericytes_Seu, k.param=80, dims=1:20)
Pericytes_Seu <- FindClusters(Pericytes_Seu, resolution = 1)
DefaultAssay(Pericytes_Seu) = "RNA"
#CheckInput = Pericytes03Clean
#CheckUMAP(Pericytes_Seu)
Pericytes_UMAP = as.data.frame(Pericytes_Seu@reductions$umap@cell.embeddings)
Pericytes01 = subset(Pericytes_Seu, idents = c(0,1,2,3))
Pericytes01Clean = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes01) & Pericytes_UMAP$UMAP_1 < -2 & Pericytes_UMAP$UMAP_2 < 6 | row.names(Pericytes_UMAP) %in% colnames(Pericytes01) & Pericytes_UMAP$UMAP_1 > -2 & Pericytes_UMAP$UMAP_2 < 10)
Pericytes02 = subset(Pericytes_Seu, idents = 4)
Pericytes02Clean = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes02) & Pericytes_UMAP$UMAP_1 < 0 & Pericytes_UMAP$UMAP_2 > 6)
Pericytes03 = subset(Pericytes_Seu, idents = c(5))
Pericytes03Clean = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes03) & Pericytes_UMAP$UMAP_1 > 0 & Pericytes_UMAP$UMAP_2 > 10)
#CheckInput = Pericytes01_Extras
#CheckUMAP(Pericytes_Seu)
Pericytes_REST = subset(Pericytes_Seu, cells = c(row.names(Pericytes01Clean), row.names(Pericytes02Clean), row.names(Pericytes03Clean)), invert=T)
Pericytes01_Extras = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 < -2 & Pericytes_UMAP$UMAP_2 < 6 | row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 > -2 & Pericytes_UMAP$UMAP_2 < 10 | row.names(Pericytes_UMAP) %in% row.names(Pericytes01Clean))
Pericytes02_Extras = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 < 0 & Pericytes_UMAP$UMAP_2 > 6 | row.names(Pericytes_UMAP) %in% row.names(Pericytes02Clean))
Pericytes03_Extras = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 > 0 & Pericytes_UMAP$UMAP_2 > 10 | row.names(Pericytes_UMAP) %in% row.names(Pericytes03Clean))
Pericytes_Assigns = GenerateMetaData_Barcodes(list("Pericytes_01" = Pericytes01_Extras, "Pericytes_02" = Pericytes02_Extras, "Pericytes_03" = Pericytes03_Extras))
#Pericytes02_Extras_Pt2 = subset(Pericytes_Seu, cells = Pericytes_Assigns$Barcodes, invert=T)
#CheckInput = Pericytes03_Extras
#CheckUMAP(Pericytes_Seu)
Pericytes_Assigns$Dups = duplicated(Pericytes_Assigns$Barcodes) | duplicated(Pericytes_Assigns$Barcodes, fromLast=T)
Pericytes_Assigns_T = subset(Pericytes_Assigns, Pericytes_Assigns$Dups == T)
unique(Pericytes_Assigns_T$Pop)
Pericytes_Assigns = GenerateMetaData(list( "Pericytes_01" = Pericytes01_Extras, "Pericytes_02" = Pericytes02_Extras, "Pericytes_03" = Pericytes03_Extras))
#load("~/Hypothalamus_Subclustering_APR2023.RData")
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
SMC
Idents(KaZhouAll) = "MainClusters"
SMC_Seu = subset(KaZhouAll, idents = "vSMC")
DefaultAssay(SMC_Seu) = "integrated"
SMC_Seu = FindVariableFeatures(SMC_Seu)
SMC_Seu = ScaleData(SMC_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(SMC_Seu) = "integrated"
SMC_Seu = RunPCA(SMC_Seu, npcs = 20)
SMC_Seu <- RunHarmony(SMC_Seu, group.by.vars = "Timepoint2")
SMC_Seu <- RunUMAP(SMC_Seu, dims = 1:20, spread= 5, reduction = "harmony")
DefaultAssay(SMC_Seu) = "RNA"
#####
set.dim = c(20)
set.res = 1
set.kparam = c(10)
#ClusterFunc_All_RNA(SMC_Seu)
DefaultAssay(SMC_Seu) = "integrated"
SMC_Seu <- FindNeighbors(SMC_Seu, k.param=10, dims=1:20)
SMC_Seu <- FindClusters(SMC_Seu, resolution = 1)
DefaultAssay(SMC_Seu) = "RNA"
#CheckInput = SMC02Clean
#CheckUMAP(SMC_Seu)
SMC_UMAP = as.data.frame(SMC_Seu@reductions$umap@cell.embeddings)
SMC01 = subset(SMC_Seu, idents = c(0,3))
SMC01Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC01) & SMC_UMAP$UMAP_1 > 5)
SMC02 = subset(SMC_Seu, idents = 1)
SMC02Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC02) & SMC_UMAP$UMAP_1 < 0 & SMC_UMAP$UMAP_2 > 5)
SMC03 = subset(SMC_Seu, idents = c(2))
SMC03Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC03) & SMC_UMAP$UMAP_1 < -10 & SMC_UMAP$UMAP_2 < 5)
SMC04 = subset(SMC_Seu, idents = 4)
SMC04Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC04) & SMC_UMAP$UMAP_1 > -10 & SMC_UMAP$UMAP_1 < 5 & SMC_UMAP$UMAP_2 < 5)
SMC_REST = subset(SMC_Seu, cells = c(row.names(SMC01Clean), row.names(SMC02Clean), row.names(SMC03Clean), row.names(SMC04Clean)), invert=T)
#CheckInput = SMC02Clean
#CheckUMAP(SMC_Seu)
SMC01_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 > 5 | row.names(SMC_UMAP) %in% row.names(SMC01Clean))
SMC02_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 < 0 & SMC_UMAP$UMAP_2 > 5 | row.names(SMC_UMAP) %in% row.names(SMC02Clean))
SMC03_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 < -10 & SMC_UMAP$UMAP_2 < 5 | row.names(SMC_UMAP) %in% row.names(SMC03Clean))
SMC04_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 > -10 & SMC_UMAP$UMAP_1 < 5 & SMC_UMAP$UMAP_2 < 5 | row.names(SMC_UMAP) %in% row.names(SMC04Clean))
SMC_Assigns = GenerateMetaData_Barcodes(list("SMC_01" = SMC01_Extras, "SMC_02" = SMC02_Extras, "SMC_03" = SMC03_Extras, "SMC_04" = SMC04_Extras))
#SMC02_Extras_Pt2 = subset(SMC_Seu, cells = SMC_Assigns$Barcodes, invert=T)
#CheckInput = SMC03_Extras
#CheckUMAP(SMC_Seu)
SMC_Assigns$Dups = duplicated(SMC_Assigns$Barcodes) | duplicated(SMC_Assigns$Barcodes, fromLast=T)
SMC_Assigns_T = subset(SMC_Assigns, SMC_Assigns$Dups == T)
unique(SMC_Assigns_T$Pop)
SMC_Assigns = GenerateMetaData(list( "SMC_01" = SMC01_Extras, "SMC_02" = SMC02_Extras, "SMC_03" = SMC03_Extras, "SMC_04" = SMC04_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Radial Glia
Idents(KaZhouAll) = "MainClusters"
RG_Seu = subset(KaZhouAll, idents = "RadialGlia")
DefaultAssay(RG_Seu) = "integrated"
RG_Seu = FindVariableFeatures(RG_Seu)
RG_Seu = ScaleData(RG_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(RG_Seu) = "integrated"
RG_Seu = RunPCA(RG_Seu, npcs = 20)
RG_Seu <- RunHarmony(RG_Seu, group.by.vars = "Timepoint2")
RG_Seu <- RunUMAP(RG_Seu, dims = 1:20, spread= 2, reduction = "harmony")
DefaultAssay(RG_Seu) = "RNA"
#####
set.dim = c(20)
set.res = c(2,3)
set.kparam = c(100)
#ClusterFunc_All_RNA(RG_Seu)
DefaultAssay(RG_Seu) = "integrated"
RG_Seu <- FindNeighbors(RG_Seu, k.param=100, dims=1:20)
RG_Seu <- FindClusters(RG_Seu, resolution = 3)
DefaultAssay(RG_Seu) = "RNA"
#CheckInput = RG09Clean
#CheckUMAP(RG_Seu)
RG_UMAP = as.data.frame(RG_Seu@reductions$umap@cell.embeddings)
RG01 = subset(RG_Seu, idents = c(0,5))
RG01Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG01) & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > -6 & RG_UMAP$UMAP_2 < -4)
RG15Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG01) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 0 & RG_UMAP$UMAP_2 < -1 & RG_UMAP$UMAP_2 > -8)
RG02 = subset(RG_Seu, idents = c(1,16,10))
RG02Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG02) & RG_UMAP$UMAP_1 > 0 & RG_UMAP$UMAP_2 > 0 & RG_UMAP$UMAP_2 < 10)
RG03 = subset(RG_Seu, idents = c(2,7))
RG03Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG03) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 > 2 & RG_UMAP$UMAP_2 < 9)
RG04 = subset(RG_Seu, idents = c(3,4,14))
RG04Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG04) & RG_UMAP$UMAP_1 > -1 & RG_UMAP$UMAP_1 < 7 & RG_UMAP$UMAP_2 > -9 & RG_UMAP$UMAP_2 < 1)
RG05 = subset(RG_Seu, idents = c(6))
RG05Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG05) &RG_UMAP$UMAP_2 > 12)
RG06 = subset(RG_Seu, idents = c(8,19))
RG06Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG06) & RG_UMAP$UMAP_1 > -6 & RG_UMAP$UMAP_1 < -2 & RG_UMAP$UMAP_2 > -4 & RG_UMAP$UMAP_2 < 1 | row.names(RG_UMAP) %in% colnames(RG06) & RG_UMAP$UMAP_1 > -2 & RG_UMAP$UMAP_1 < 2 & RG_UMAP$UMAP_2 > -1 & RG_UMAP$UMAP_2 < 2)
RG07 = subset(RG_Seu, idents = c(9,11,20))
RG07Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG07) & RG_UMAP$UMAP_1 > 2 & RG_UMAP$UMAP_2 < -4)
RG08 = subset(RG_Seu, idents = c(12,15))
RG08Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG08) & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > 2)
RG09 = subset(RG_Seu, idents = c(13,18))
RG09Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG09) & RG_UMAP$UMAP_1 > -3.2 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 < -4)
RG10 = subset(RG_Seu, idents = c(17))
RG10Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG10) & RG_UMAP$UMAP_1 > -9 & RG_UMAP$UMAP_1 < -5 & RG_UMAP$UMAP_2 < -5.5)
RG11 = subset(RG_Seu, idents = 21)
RG11Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG11) & RG_UMAP$UMAP_2 > 10 & RG_UMAP$UMAP_2 < 14)
RG12 = subset(RG_Seu, idents = 22)
RG12Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG12) & RG_UMAP$UMAP_1 < -5 & RG_UMAP$UMAP_2 < 0)
RG13 = subset(RG_Seu, idents = 23)
RG13Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG13) & RG_UMAP$UMAP_1 < -5 )
RG14 = subset(RG_Seu, idents = 24)
RG14Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG14) & RG_UMAP$UMAP_2 < 0)
#CheckInput = RG14Clean
#CheckUMAP(RG_Seu)
RG_REST = subset(RG_Seu, cells = c(row.names(RG01Clean), row.names(RG02Clean), row.names(RG03Clean), row.names(RG04Clean), row.names(RG05Clean), row.names(RG06Clean), row.names(RG07Clean), row.names(RG08Clean), row.names(RG09Clean), row.names(RG10Clean), row.names(RG11Clean), row.names(RG12Clean), row.names(RG13Clean), row.names(RG14Clean), row.names(RG15Clean)), invert=T)
#CheckInput = RG02_Extras
#CheckUMAP(RG_Seu)
RG01_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > -6 & RG_UMAP$UMAP_2 < -4 | row.names(RG_UMAP) %in% row.names(RG01Clean))
RG02_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > 3 & RG_UMAP$UMAP_2 > 1.2 & RG_UMAP$UMAP_2 < 10 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -1 & RG_UMAP$UMAP_2 > 1 & RG_UMAP$UMAP_2 < 4 & RG_UMAP$UMAP_2 < 3 | row.names(RG_UMAP) %in% row.names(RG02Clean))
RG03_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 > 3 & RG_UMAP$UMAP_2 < 9 & ! row.names(RG_UMAP) %in% c(row.names(RG02_Extras)) | row.names(RG_UMAP) %in% row.names(RG03Clean))
RG05_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_2 > 13.5 | row.names(RG_UMAP) %in% row.names(RG05Clean))
RG06_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -5.5 & RG_UMAP$UMAP_1 < -3 & RG_UMAP$UMAP_2 > -2.5 & RG_UMAP$UMAP_2 < 1.5 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -3 & RG_UMAP$UMAP_1 < 0 & RG_UMAP$UMAP_2 > -3.5 & RG_UMAP$UMAP_2 < 2 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -2 & RG_UMAP$UMAP_1 < 1.5 & RG_UMAP$UMAP_2 > -1 & RG_UMAP$UMAP_2 < 2 & ! row.names(RG_UMAP) %in% row.names(RG02_Extras) | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -7.7 & RG_UMAP$UMAP_1 < -5.5 & RG_UMAP$UMAP_2 > -1 & RG_UMAP$UMAP_2 < 1 | row.names(RG_UMAP) %in% row.names(RG06Clean))
RG07_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > 3.5 & RG_UMAP$UMAP_2 < -4| row.names(RG_UMAP) %in% row.names(RG07Clean))
RG08_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 < -11 & RG_UMAP$UMAP_2 > 2 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -11 & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > 4| row.names(RG_UMAP) %in% row.names(RG08Clean))
RG09_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -2.5 & RG_UMAP$UMAP_1 < 1 & RG_UMAP$UMAP_2 < -5 & ! row.names(RG_UMAP) %in% row.names(RG07_Extras) | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > 1 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 < -8 & ! row.names(RG_UMAP) %in% row.names(RG07_Extras)| row.names(RG_UMAP) %in% row.names(RG09Clean))
RG11_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_2 > 10 & ! row.names(RG_UMAP) %in% c(row.names(RG05_Extras)) | row.names(RG_UMAP) %in% row.names(RG11Clean))
RG14_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -5 & RG_UMAP$UMAP_1 < -3 & RG_UMAP$UMAP_2 < -8 | row.names(RG_UMAP) %in% row.names(RG14Clean))
RG10_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -9 & RG_UMAP$UMAP_1 < -4.7 & RG_UMAP$UMAP_2 < -4 & ! row.names(RG_UMAP) %in% c(row.names(RG01_Extras), row.names(RG14_Extras)) | row.names(RG_UMAP) %in% row.names(RG10Clean))
RG12_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 < -5 & RG_UMAP$UMAP_2 < 0 & ! row.names(RG_UMAP) %in% c(row.names(RG01_Extras), row.names(RG06_Extras), row.names(RG10_Extras)) | row.names(RG_UMAP) %in% row.names(RG12Clean))
RG15_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 0 & RG_UMAP$UMAP_2 < -1 & RG_UMAP$UMAP_2 > -8 & ! row.names(RG_UMAP) %in% c(row.names(RG06_Extras), row.names(RG08_Extras), row.names(RG10_Extras), row.names(RG09_Extras), row.names(RG12_Extras)) | row.names(RG_UMAP) %in% row.names(RG15Clean))
RG13_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -12 & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > -0.5 & RG_UMAP$UMAP_2 < 5 & ! row.names(RG_UMAP) %in% c(row.names(RG06_Extras), row.names(RG08_Extras), row.names(RG12_Extras)) | row.names(RG_UMAP) %in% row.names(RG13Clean))
RG04_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -0.5 & RG_UMAP$UMAP_1 < 8 & RG_UMAP$UMAP_2 > -4 & RG_UMAP$UMAP_2 < 1.2 & ! row.names(RG_UMAP) %in% c(row.names(RG02_Extras), row.names(RG06_Extras), row.names(RG07_Extras), row.names(RG09_Extras), row.names(RG15_Extras)) | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -0.5 & RG_UMAP$UMAP_1 < 6 & RG_UMAP$UMAP_2 > -8 & RG_UMAP$UMAP_2 < 1.2 & ! row.names(RG_UMAP) %in% c(row.names(RG02_Extras), row.names(RG06_Extras), row.names(RG07_Extras), row.names(RG09_Extras), row.names(RG15_Extras)) | row.names(RG_UMAP) %in% row.names(RG04Clean))
#CheckInput = RG06_Extras
#CheckUMAP(RG_Seu)
RG_Assigns = GenerateMetaData_Barcodes(list("RG_01" = RG01_Extras, "RG_02" = RG02_Extras, "RG_03" = RG03_Extras, "RG_04" = RG04_Extras, "RG_05" = RG05_Extras, "RG_06" = RG06_Extras, "RG_07" = RG07_Extras, "RG_08" = RG08_Extras, "RG_09" = RG09_Extras, "RG_10" = RG10_Extras, "RG_11" = RG11_Extras, "RG_12" = RG12_Extras, "RG_13" = RG13_Extras, "RG_14" = RG14_Extras, "RG_15" = RG15_Extras))
RG02_Extras_Pt2 = subset(RG_Seu, cells = RG_Assigns$Barcodes, invert=T)
#CheckInput = RG02_Extras_Pt2
#CheckUMAP(RG_Seu)
RG_Assigns$Dups = duplicated(RG_Assigns$Barcodes) | duplicated(RG_Assigns$Barcodes, fromLast=T)
RG_Assigns_T = subset(RG_Assigns, RG_Assigns$Dups == T)
unique(RG_Assigns_T$Pop)
RG_Assigns = GenerateMetaData(list( "RG_01" = RG01_Extras, "RG_02" = RG02_Extras, "RG_03" = RG03_Extras, "RG_04" = RG04_Extras, "RG_05" = RG05_Extras, "RG_06" = RG06_Extras, "RG_07" = RG07_Extras, "RG_08" = RG08_Extras, "RG_09" = RG09_Extras, "RG_10" = RG10_Extras, "RG_11" = RG11_Extras, "RG_12" = RG12_Extras, "RG_13" = RG13_Extras, "RG_14" = RG14_Extras, "RG_15" = RG15_Extras, "RG_06" = RG02_Extras_Pt2))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Neurepithelial
Idents(KaZhouAll) = "MainClusters"
NE_Seu = subset(KaZhouAll, idents = "Neuroepithelial")
DefaultAssay(NE_Seu) = "integrated"
NE_Seu = FindVariableFeatures(NE_Seu)
NE_Seu = ScaleData(NE_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(NE_Seu) = "integrated"
NE_Seu = RunPCA(NE_Seu, npcs = 20)
#NE_Seu <- RunHarmony(NE_Seu, group.by.vars = "Timepoint2")
NE_Seu <- RunUMAP(NE_Seu, dims = 1:20, spread= 2)#, reduction = "harmony")
DefaultAssay(NE_Seu) = "RNA"
#####
set.dim = c(20)
set.res = c(1)
set.kparam = c(100)
#ClusterFunc_All_RNA(NE_Seu)
DefaultAssay(NE_Seu) = "integrated"
NE_Seu <- FindNeighbors(NE_Seu, k.param=100, dims=1:20)
NE_Seu <- FindClusters(NE_Seu, resolution = 1)
DefaultAssay(NE_Seu) = "RNA"
#CheckInput = NE09
#CheckUMAP(NE_Seu)
NE_UMAP = as.data.frame(NE_Seu@reductions$umap@cell.embeddings)
NE01 = subset(NE_Seu, idents = c(0,4,8))
NE01Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE01) & NE_UMAP$UMAP_2 > 1 & NE_UMAP$UMAP_1 > -2)
NE02 = subset(NE_Seu, idents = c(1))
NE02Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE02) & NE_UMAP$UMAP_1 > 5 & NE_UMAP$UMAP_2 < 5 | row.names(NE_UMAP) %in% colnames(NE02) & NE_UMAP$UMAP_1 > 1 & NE_UMAP$UMAP_2 > -5 & NE_UMAP$UMAP_2 < 5)
NE03 = subset(NE_Seu, idents = c(2,9))
NE03Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE03) & NE_UMAP$UMAP_1 > -13 & NE_UMAP$UMAP_1 < -4 & NE_UMAP$UMAP_2 > -12 & NE_UMAP$UMAP_2 < 5)
NE04 = subset(NE_Seu, idents = c(3,7,11))
NE04Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE04) & NE_UMAP$UMAP_1 > -8 & NE_UMAP$UMAP_1 < 8 & NE_UMAP$UMAP_2 > -7 & NE_UMAP$UMAP_2 < 2)
NE05 = subset(NE_Seu, idents = c(5))
NE05Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE05) &NE_UMAP$UMAP_2 < -11)
NE06 = subset(NE_Seu, idents = c(6))
NE06Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE06) & NE_UMAP$UMAP_1 > -10 & NE_UMAP$UMAP_1 < 7 & NE_UMAP$UMAP_2 > -11 & NE_UMAP$UMAP_2 < -3 )
NE07 = subset(NE_Seu, idents = c(10))
NE07Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE07) & NE_UMAP$UMAP_2 > 11)
NE08 = subset(NE_Seu, idents = c(12))
NE09 = subset(NE_Seu, idents = c(13,14))
NE_REST = subset(NE_Seu, cells = c(row.names(NE01Clean), row.names(NE02Clean), row.names(NE03Clean), row.names(NE04Clean), row.names(NE05Clean), row.names(NE06Clean), row.names(NE07Clean), colnames(NE08), colnames(NE09)), invert=T)
#CheckInput = NE06_Extras
#CheckUMAP(NE_Seu)
NE01_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > -5 & NE_UMAP$UMAP_2 > 3 & NE_UMAP$UMAP_2 < 13 | row.names(NE_UMAP) %in% row.names(NE01Clean))
NE02_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > 5 & NE_UMAP$UMAP_2 < -2 | row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > 3 & NE_UMAP$UMAP_2 > -6 & NE_UMAP$UMAP_2 < 2 | row.names(NE_UMAP) %in% row.names(NE02Clean))
NE03_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > -12 & NE_UMAP$UMAP_1 < -6 & NE_UMAP$UMAP_2 > -12 & NE_UMAP$UMAP_2 < 5 | row.names(NE_UMAP) %in% row.names(NE03Clean))
NE04_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST)& NE_UMAP$UMAP_1 > -8 & NE_UMAP$UMAP_1 < 8 & NE_UMAP$UMAP_2 > -7 & NE_UMAP$UMAP_2 < 3 & ! row.names(NE_UMAP) %in% c(row.names(NE03_Extras), row.names(NE02_Extras), row.names(NE01_Extras)) | row.names(NE_UMAP) %in% row.names(NE04Clean))
#& ! row.names(NE_UMAP) %in% c(row.names(NE02_Extras))
NE05_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_2 < -12 | row.names(NE_UMAP) %in% row.names(NE05Clean))
NE06_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST)& NE_UMAP$UMAP_1 > -10 & NE_UMAP$UMAP_1 < 7 & NE_UMAP$UMAP_2 > -11 & NE_UMAP$UMAP_2 < -3 & ! row.names(NE_UMAP) %in% c(row.names(NE03_Extras), row.names(NE02_Extras), row.names(NE04_Extras))| row.names(NE_UMAP) %in% row.names(NE06Clean))
NE07_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > 5 & NE_UMAP$UMAP_2 > 12 | row.names(NE_UMAP) %in% row.names(NE07Clean))
NE08_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 < 0 & NE_UMAP$UMAP_2 > 10 | row.names(NE_UMAP) %in% colnames(NE08))
NE09_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 < -12 | row.names(NE_UMAP) %in% colnames(NE09))
#CheckInput = NE09_Extras
#CheckUMAP(NE_Seu)
NE_Assigns = GenerateMetaData_Barcodes(list("NE_01" = NE01_Extras, "NE_02" = NE02_Extras, "NE_03" = NE03_Extras, "NE_04" = NE04_Extras, "NE_05" = NE05_Extras, "NE_06" = NE06_Extras, "NE_07" = NE07_Extras, "NE_08" = NE08_Extras, "NE_09" = NE09_Extras))
#NE02_Extras_Pt2 = subset(NE_Seu, cells = NE_Assigns$Barcodes, invert=T)
#CheckInput = NE02_Extras_Pt2
#CheckUMAP(NE_Seu)
NE_Assigns$Dups = duplicated(NE_Assigns$Barcodes) | duplicated(NE_Assigns$Barcodes, fromLast=T)
NE_Assigns_T = subset(NE_Assigns, NE_Assigns$Dups == T)
unique(NE_Assigns_T$Pop)
NE_Assigns = GenerateMetaData(list( "NE_01" = NE01_Extras, "NE_02" = NE02_Extras, "NE_03" = NE03_Extras, "NE_04" = NE04_Extras, "NE_05" = NE05_Extras, "NE_06" = NE06_Extras, "NE_07" = NE07_Extras, "NE_08" = NE08_Extras, "NE_09" = NE09_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Intermediate Progenitors
Idents(KaZhouAll) = "MainClusters"
IP_Seu = subset(KaZhouAll, idents = "IntProgen")
DefaultAssay(IP_Seu) = "integrated"
IP_Seu = FindVariableFeatures(IP_Seu)
IP_Seu = ScaleData(IP_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(IP_Seu) = "integrated"
IP_Seu = RunPCA(IP_Seu, npcs = 30)
IP_Seu <- RunHarmony(IP_Seu, group.by.vars = "Timepoint2")
IP_Seu <- RunUMAP(IP_Seu, dims = 1:30, spread= 5, reduction = "harmony")
DefaultAssay(IP_Seu) = "RNA"
#####
set.dim = c(30)
set.res = c(1)
set.kparam = c(20,50, 100)
#ClusterFunc_All_RNA(IP_Seu)
DefaultAssay(IP_Seu) = "integrated"
IP_Seu <- FindNeighbors(IP_Seu, k.param=50, dims=1:30)
IP_Seu <- FindClusters(IP_Seu, resolution = 1)
DefaultAssay(IP_Seu) = "RNA"
#CheckInput = IP01Clean
#CheckUMAP(IP_Seu)
IP_UMAP = as.data.frame(IP_Seu@reductions$umap@cell.embeddings)
IP01 = subset(IP_Seu, idents = c(0,1,7,9))
IP01Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP01) & IP_UMAP$UMAP_2 < 0 & IP_UMAP$UMAP_1 < -5 | row.names(IP_UMAP) %in% colnames(IP01) & IP_UMAP$UMAP_2 > -3 & IP_UMAP$UMAP_2 < 3 & IP_UMAP$UMAP_1 > -20 & IP_UMAP$UMAP_1 < 3 | row.names(IP_UMAP) %in% colnames(IP01) & IP_UMAP$UMAP_2 > 3 & IP_UMAP$UMAP_2 < 14 & IP_UMAP$UMAP_1 > -15 & IP_UMAP$UMAP_1 < 7 )
IP02 = subset(IP_Seu, idents = c(2,3,5))
IP02Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP02) & IP_UMAP$UMAP_1 > -7 & IP_UMAP$UMAP_2 < -5 | row.names(IP_UMAP) %in% colnames(IP02) & IP_UMAP$UMAP_1 > 5 & IP_UMAP$UMAP_2 < 5| row.names(IP_UMAP) %in% colnames(IP02) & IP_UMAP$UMAP_1 > 10)
IP03 = subset(IP_Seu, idents = c(4,8))
IP03Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP03) & IP_UMAP$UMAP_1 < 3 & IP_UMAP$UMAP_2 > 10)
IP04 = subset(IP_Seu, idents = c(6))
IP04Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP04) & IP_UMAP$UMAP_1 < -20)
IP_REST = subset(IP_Seu, cells = c(row.names(IP01Clean), row.names(IP02Clean), row.names(IP03Clean), row.names(IP04Clean)), invert=T)
#CheckInput = IP01_Extras
#CheckUMAP(IP_Seu)
IP01_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_2 < 0 & IP_UMAP$UMAP_1 < -5 | row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_2 > -5 & IP_UMAP$UMAP_2 < 4 & IP_UMAP$UMAP_1 > -20 & IP_UMAP$UMAP_1 < 3 | row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_2 > 2 & IP_UMAP$UMAP_2 < 14 & IP_UMAP$UMAP_1 > -15 & IP_UMAP$UMAP_1 < 5 | row.names(IP_UMAP) %in% row.names(IP01Clean))
IP02_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 > -7 & IP_UMAP$UMAP_2 < -5 |row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 > 4 & IP_UMAP$UMAP_2 < 5| row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 > 10 | row.names(IP_UMAP) %in% row.names(IP02Clean))
IP03_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 < 3 & IP_UMAP$UMAP_2 > 10 & ! row.names(IP_UMAP) %in% row.names(IP01_Extras) | row.names(IP_UMAP) %in% row.names(IP03Clean))
IP04_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 < -20 & IP_UMAP$UMAP_2 > 0 | row.names(IP_UMAP) %in% row.names(IP04Clean))
#CheckInput = IP04_Extras
#CheckUMAP(IP_Seu)
IP_Assigns = GenerateMetaData_Barcodes(list("IP_01" = IP01_Extras, "IP_02" = IP02_Extras, "IP_03" = IP03_Extras, "IP_04" = IP04_Extras))
#IP02_Extras_Pt2 = subset(IP_Seu, cells = IP_Assigns$Barcodes, invert=T)
#CheckInput = IP02_Extras_Pt2
#CheckUMAP(IP_Seu)
IP_Assigns$Dups = duplicated(IP_Assigns$Barcodes) | duplicated(IP_Assigns$Barcodes, fromLast=T)
IP_Assigns_T = subset(IP_Assigns, IP_Assigns$Dups == T)
unique(IP_Assigns_T$Pop)
IP_Assigns = GenerateMetaData(list( "IP_01" = IP01_Extras, "IP_02" = IP02_Extras, "IP_03" = IP03_Extras, "IP_04" = IP04_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Dividing Progenitors
Idents(KaZhouAll) = "MainClusters"
Div_Seu = subset(KaZhouAll, idents = "Dividing")
DefaultAssay(Div_Seu) = "integrated"
Div_Seu = FindVariableFeatures(Div_Seu)
Div_Seu = ScaleData(Div_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Div_Seu) = "integrated"
Div_Seu = RunPCA(Div_Seu, npcs = 20)
#Div_Seu <- RunHarmony(Div_Seu, group.by.vars = "Timepoint2")
Div_Seu <- RunUMAP(Div_Seu, dims = 1:20, spread= 2)#, reduction = "harmony")
DefaultAssay(Div_Seu) = "RNA"
#####
set.dim = c(20)
set.res = c(1)
set.kparam = c(50, 20)
#ClusterFunc_All_RNA(Div_Seu)
DefaultAssay(Div_Seu) = "integrated"
Div_Seu <- FindNeighbors(Div_Seu, k.param=20, dims=1:20)
Div_Seu <- FindClusters(Div_Seu, resolution = 1)
DefaultAssay(Div_Seu) = "RNA"
#CheckInput = Div02Clean
#CheckUMAP(Div_Seu)
Div_UMAP = as.data.frame(Div_Seu@reductions$umap@cell.embeddings)
Div01 = subset(Div_Seu, idents = c(0)) #Neuro-Astro
Div01Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div01) & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -1 & Div_UMAP$UMAP_2 < 3 | row.names(Div_UMAP) %in% colnames(Div01) & Div_UMAP$UMAP_1 > -5 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -3 & Div_UMAP$UMAP_2 < 1)
Div02 = subset(Div_Seu, idents = c(1)) #Neuro
Div02Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div02) & Div_UMAP$UMAP_1 > -11 & Div_UMAP$UMAP_1 < -6 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 2 | row.names(Div_UMAP) %in% colnames(Div02) & Div_UMAP$UMAP_1 > -14 & Div_UMAP$UMAP_1 < -11 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 1.)
Div03 = subset(Div_Seu, idents = c(2,16)) #No lineage
Div03Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div03) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_2 < -9 | row.names(Div_UMAP) %in% colnames(Div03) & Div_UMAP$UMAP_1 > 5 & Div_UMAP$UMAP_2 < 0)
Div04 = subset(Div_Seu, idents = c(3)) #Astro
Div04Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div04) & Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_1 < 9)
Div05 = subset(Div_Seu, idents = c(4)) #Fibroblasts
Div05Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div05) & Div_UMAP$UMAP_1 > 6 & Div_UMAP$UMAP_2 >7)
Div06 = subset(Div_Seu, idents = c(5)) #Fibroblasts
Div06Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div06) & Div_UMAP$UMAP_1 > -6 & Div_UMAP$UMAP_1 < 4 & Div_UMAP$UMAP_2 > 4 & Div_UMAP$UMAP_2 < 9)
Div07 = subset(Div_Seu, idents = c(6)) #Fibroblasts
Div07Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div07) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_1 < 10 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 5)
Div08 = subset(Div_Seu, idents = c(7)) #Neuro
Div08Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div08) & Div_UMAP$UMAP_2 < -10 & Div_UMAP$UMAP_1 < 8)
Div09 = subset(Div_Seu, idents = c(8)) #Fibroblasts
Div09Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div09) & Div_UMAP$UMAP_1 > 5 & Div_UMAP$UMAP_1 < 11 & Div_UMAP$UMAP_2 > 1.7 & Div_UMAP$UMAP_2 < 10)
Div10 = subset(Div_Seu, idents = c(9)) #Neuro
Div10Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div10) & Div_UMAP$UMAP_1 > 0.5 & Div_UMAP$UMAP_1 < 7.5 & Div_UMAP$UMAP_2 > 0 & Div_UMAP$UMAP_2 < 9)
Div11 = subset(Div_Seu, idents = c(10,15,23))
Div11Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div11) & Div_UMAP$UMAP_1 > -7 & Div_UMAP$UMAP_1 < 0 & Div_UMAP$UMAP_2 > 6 & Div_UMAP$UMAP_2 < 10.5) #No Lineage
Div12Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div11) & Div_UMAP$UMAP_1 < -7 & Div_UMAP$UMAP_2 > -1 & Div_UMAP$UMAP_2 < 7) #Fibroblasts
Div13 = subset(Div_Seu, idents = c(11)) #Neuro-Olig
Div13Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div13) & Div_UMAP$UMAP_1 < -10 & Div_UMAP$UMAP_2 < -2)
Div14 = subset(Div_Seu, idents = c(12))
Div14Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div14) & Div_UMAP$UMAP_1 > -1& Div_UMAP$UMAP_1 < 2.5 & Div_UMAP$UMAP_2 < 2) #Neuro
Div15Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div14) & Div_UMAP$UMAP_1 > 2.5 & Div_UMAP$UMAP_2 < 2) #No lineage
Div16 = subset(Div_Seu, idents = c(13,20)) #Neuro-Astro
Div16Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div16) & Div_UMAP$UMAP_1 > -13 & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_2 < 0)
Div17 = subset(Div_Seu, idents = c(14, 18)) #Fibroblasts
Div17Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div17) & Div_UMAP$UMAP_1 > -1 & Div_UMAP$UMAP_2 > 2)
Div18 = subset(Div_Seu, idents = c(17)) #TAGLN/DCN
Div18Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div18) & Div_UMAP$UMAP_2 > 10)
Div19 = subset(Div_Seu, idents = c(19)) #Astro
Div19Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div19) & Div_UMAP$UMAP_1 > -9 & Div_UMAP$UMAP_1 < 0 & Div_UMAP$UMAP_2 < -2)
Div20 = subset(Div_Seu, idents = c(21)) #Olig
Div20Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div20) & Div_UMAP$UMAP_1 > -9 & Div_UMAP$UMAP_1 < 0)
Div21 = subset(Div_Seu, idents = c(22)) #Astro
Div21Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div21) & Div_UMAP$UMAP_2 < -5)
Div22 = subset(Div_Seu, idents = c(24)) #Fibroblasts
Div23 = subset(Div_Seu, idents = c(25)) #Fibroblasts
Div23Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div23) & Div_UMAP$UMAP_1 > 11)
#CheckInput = Div23Clean
#CheckUMAP(Div_Seu)
Div_REST = subset(Div_Seu, cells = c(row.names(Div01Clean), row.names(Div02Clean), row.names(Div03Clean), row.names(Div04Clean), row.names(Div05Clean), row.names(Div06Clean), row.names(Div07Clean), row.names(Div08Clean), row.names(Div09Clean), row.names(Div10Clean), row.names(Div11Clean), row.names(Div12Clean), row.names(Div13Clean), row.names(Div14Clean), row.names(Div15Clean), row.names(Div16Clean), row.names(Div17Clean), row.names(Div18Clean), row.names(Div19Clean), row.names(Div20Clean), row.names(Div21Clean),colnames(Div22), row.names(Div23Clean)), invert=T)
#CheckInput = Div02_Extras
#CheckUMAP(Div_Seu)
Div01_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -6 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -1 & Div_UMAP$UMAP_2 < 3 | row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -5 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -3 & Div_UMAP$UMAP_2 < 1 | row.names(Div_UMAP) %in% row.names(Div01Clean))
Div02_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_2 > -3 & Div_UMAP$UMAP_2 < 0 & ! row.names(Div_UMAP) %in% row.names(Div01_Extras)| row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -13 & Div_UMAP$UMAP_1 < -6 & Div_UMAP$UMAP_2 > -3.5 & Div_UMAP$UMAP_2 < 2 | row.names(Div_UMAP) %in% row.names(Div02Clean))
Div03_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_2 < -9 | row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 7 & Div_UMAP$UMAP_2 < 0 | row.names(Div_UMAP) %in% row.names(Div03Clean))
Div05_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 6 & Div_UMAP$UMAP_2 > 9 | row.names(Div_UMAP) %in% row.names(Div05Clean))
Div07_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_1 < 10 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 3 & ! row.names(Div_UMAP) %in% row.names(Div03_Extras) | row.names(Div_UMAP) %in% row.names(Div07Clean))
Div08_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_2 < -13 & ! row.names(Div_UMAP) %in% row.names(Div03_Extras) | row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_1 < 3 & Div_UMAP$UMAP_2 < -11 & ! row.names(Div_UMAP) %in% row.names(Div03_Extras) | row.names(Div_UMAP) %in% row.names(Div08Clean))
Div09_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 7 & Div_UMAP$UMAP_1 < 11 & Div_UMAP$UMAP_2 > 2 & Div_UMAP$UMAP_2 < 7 & ! row.names(Div_UMAP) %in% row.names(Div07_Extras)| row.names(Div_UMAP) %in% row.names(Div09Clean))
Div10_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 >1 & Div_UMAP$UMAP_1 < 6 & Div_UMAP$UMAP_2 > 2 & Div_UMAP$UMAP_2 < 6 | row.names(Div_UMAP) %in% row.names(Div10Clean))
Div12_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 < -8 & Div_UMAP$UMAP_2 > -2 & Div_UMAP$UMAP_2 < 9 & ! row.names(Div_UMAP) %in% row.names(Div02_Extras) | row.names(Div_UMAP) %in% row.names(Div12Clean))
Div13_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 < -10 & Div_UMAP$UMAP_2 < -2 & ! row.names(Div_UMAP) %in% row.names(Div02_Extras) | row.names(Div_UMAP) %in% row.names(Div13Clean))
Div14_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -0.5 & Div_UMAP$UMAP_1 < 2.5 & Div_UMAP$UMAP_2 < 0 & Div_UMAP$UMAP_2 > -6 & ! row.names(Div_UMAP) %in% row.names(Div01_Extras) | row.names(Div_UMAP) %in% row.names(Div14Clean))
Div15_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 2.5 & Div_UMAP$UMAP_1 < 7.5 & Div_UMAP$UMAP_2 < -2 & Div_UMAP$UMAP_2 > -7 & ! row.names(Div_UMAP) %in% c(row.names(Div03_Extras), row.names(Div07_Extras)) | row.names(Div_UMAP) %in% row.names(Div15Clean))
Div04_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST)& Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_2 > -10 & Div_UMAP$UMAP_2 < -5 & ! row.names(Div_UMAP) %in% c(row.names(Div03_Extras), row.names(Div14_Extras), row.names(Div15_Extras)) | row.names(Div_UMAP) %in% row.names(Div04Clean))
Div16_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -12 & Div_UMAP$UMAP_1 < -3 & Div_UMAP$UMAP_2 < -3 & Div_UMAP$UMAP_2 > -6 & ! row.names(Div_UMAP) %in% c(row.names(Div02_Extras), row.names(Div13_Extras)) | row.names(Div_UMAP) %in% row.names(Div16Clean))
Div17_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 1 & Div_UMAP$UMAP_1 < 10 & Div_UMAP$UMAP_2 < 11 & Div_UMAP$UMAP_2 > 6 & ! row.names(Div_UMAP) %in% c(row.names(Div05_Extras), row.names(Div09_Extras)) | row.names(Div_UMAP) %in% row.names(Div17Clean))
Div18_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_1 < 3 & Div_UMAP$UMAP_2 > 10.5 | row.names(Div_UMAP) %in% row.names(Div18Clean))
Div19_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_2 < -3 & ! row.names(Div_UMAP) %in% c(row.names(Div02_Extras), row.names(Div16_Extras))| row.names(Div_UMAP) %in% row.names(Div19Clean))
Div20_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -9 & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_2 > 2 & Div_UMAP$UMAP_2 < 5 & ! row.names(Div_UMAP) %in% c(row.names(Div01_Extras), row.names(Div05_Extras))| row.names(Div_UMAP) %in% row.names(Div20Clean))
Div06_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -7 & Div_UMAP$UMAP_1 < 3 & Div_UMAP$UMAP_2 > 4 & Div_UMAP$UMAP_2 < 9 & ! row.names(Div_UMAP) %in% c(row.names(Div10_Extras), row.names(Div20_Extras)) | row.names(Div_UMAP) %in% row.names(Div06Clean))
Div11_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 >-7 & Div_UMAP$UMAP_1 < 0 & Div_UMAP$UMAP_2 > 5 & Div_UMAP$UMAP_2 < 11 & ! row.names(Div_UMAP) %in% row.names(Div06_Extras) | row.names(Div_UMAP) %in% row.names(Div11Clean))
Div21_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_2 < -9 & ! row.names(Div_UMAP) %in% c(row.names(Div03_Extras), row.names(Div04_Extras), row.names(Div08_Extras))| row.names(Div_UMAP) %in% row.names(Div21Clean))
Div22_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 9 & Div_UMAP$UMAP_1 < 11 & Div_UMAP$UMAP_2 > 6 & Div_UMAP$UMAP_2 < 8 & ! row.names(Div_UMAP) %in% c(row.names(Div07_Extras), row.names(Div17_Extras), row.names(Div05_Extras))| row.names(Div_UMAP) %in% colnames(Div22))
Div23_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 11 | row.names(Div_UMAP) %in% row.names(Div23Clean))
#CheckInput = Div07_Extras
#CheckUMAP(Div_Seu)
Div_Assigns = GenerateMetaData_Barcodes(list("Div_01" = Div01_Extras, "Div_02" = Div02_Extras, "Div_03" = Div03_Extras, "Div_04" = Div04_Extras, "Div_05" = Div05_Extras, "Div_06" = Div06_Extras, "Div_07" = Div07_Extras, "Div_08" = Div08_Extras, "Div_09" = Div09_Extras, "Div_10" = Div10_Extras, "Div_11" = Div11_Extras, "Div_12" = Div12_Extras, "Div_13" = Div13_Extras, "Div_14" = Div14_Extras, "Div_15" = Div15_Extras, "Div_16" = Div16_Extras, "Div_17" = Div17_Extras, "Div_18" = Div18_Extras, "Div_19" = Div19_Extras, "Div_20" = Div20_Extras, "Div_21" = Div21_Extras, "Div_22" = Div22_Extras, "Div_23" = Div23_Extras))
Div02_Extras_Pt2 = subset(Div_Seu, cells = Div_Assigns$Barcodes, invert=T)
#CheckInput = Div02_Extras_Pt2
#CheckUMAP(Div_Seu)
Div_Assigns$Dups = duplicated(Div_Assigns$Barcodes) | duplicated(Div_Assigns$Barcodes, fromLast=T)
Div_Assigns_T = subset(Div_Assigns, Div_Assigns$Dups == T)
unique(Div_Assigns_T$Pop)
Div_Assigns = GenerateMetaData(list("Div_01" = Div01_Extras, "Div_02" = Div02_Extras, "Div_03" = Div03_Extras, "Div_04" = Div04_Extras, "Div_05" = Div05_Extras, "Div_06" = Div06_Extras, "Div_07" = Div07_Extras, "Div_08" = Div08_Extras, "Div_09" = Div09_Extras, "Div_10" = Div10_Extras, "Div_11" = Div11_Extras, "Div_12" = Div12_Extras, "Div_13" = Div13_Extras, "Div_14" = Div14_Extras, "Div_15" = Div15_Extras, "Div_16" = Div16_Extras, "Div_17" = Div17_Extras, "Div_18" = Div18_Extras, "Div_19" = Div19_Extras, "Div_20" = Div20_Extras, "Div_21" = Div21_Extras, "Div_22" = Div22_Extras, "Div_23" = Div23_Extras, "Div_10" = Div02_Extras_Pt2))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Astrocytes
Idents(KaZhouAll) = "MainClusters"
Astro_Seu = subset(KaZhouAll, idents = "Astrocytes")
DefaultAssay(Astro_Seu) = "integrated"
Astro_Seu = FindVariableFeatures(Astro_Seu)
Astro_Seu = ScaleData(Astro_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Astro_Seu) = "integrated"
Astro_Seu = RunPCA(Astro_Seu, npcs = 20)
#Astro_Seu <- RunHarmony(Astro_Seu, group.by.vars = "Timepoint2")
Astro_Seu <- RunUMAP(Astro_Seu, dims = 1:20, spread= 2)#, reduction = "harmony")
DefaultAssay(Astro_Seu) = "RNA"
#####
set.dim = c(20)
set.res = c(1)
set.kparam = c(50,100)
##ClusterFunc_All_RNA(Astro_Seu)
DefaultAssay(Astro_Seu) = "integrated"
Astro_Seu <- FindNeighbors(Astro_Seu, k.param=100, dims=1:20)
Astro_Seu <- FindClusters(Astro_Seu, resolution = 1)
DefaultAssay(Astro_Seu) = "RNA"
##CheckInput = Astro06Clean
#CheckUMAP(Astro_Seu)
Astro_UMAP = as.data.frame(Astro_Seu@reductions$umap@cell.embeddings)
Astro01 = subset(Astro_Seu, idents = c(4))
Astro01Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro01) & Astro_UMAP$UMAP_1 < 0 & Astro_UMAP$UMAP_2 > 1)
Astro02 = subset(Astro_Seu, idents = c(2,11))
Astro02Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro02) & Astro_UMAP$UMAP_1 > 4 & Astro_UMAP$UMAP_2 > -5 & Astro_UMAP$UMAP_2 < 2.5)
Astro03 = subset(Astro_Seu, idents = c(9))
Astro03Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro03) & Astro_UMAP$UMAP_1 > 0)
Astro04 = subset(Astro_Seu, idents = c(8))
Astro04Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro04) & Astro_UMAP$UMAP_1 > 6)
Astro05 = subset(Astro_Seu, idents = c(12))
Astro06 = subset(Astro_Seu, idents = c(0,1,3,5,6,7,10))
Astro06Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro06) & Astro_UMAP$UMAP_1 < 0 & Astro_UMAP$UMAP_2 < -1.5 | row.names(Astro_UMAP) %in% colnames(Astro06) & Astro_UMAP$UMAP_1 > -7.5 & Astro_UMAP$UMAP_1 < 5 & Astro_UMAP$UMAP_2 > -4 & Astro_UMAP$UMAP_2 < 4 | row.names(Astro_UMAP) %in% colnames(Astro06) & Astro_UMAP$UMAP_1 > -4 & Astro_UMAP$UMAP_1 < 5.5 & Astro_UMAP$UMAP_2 > -5)
Astro_REST = subset(Astro_Seu, cells = c(row.names(Astro01Clean), row.names(Astro02Clean), row.names(Astro03Clean), row.names(Astro04Clean), colnames(Astro05), row.names(Astro06Clean)), invert=T)
#CheckInput = Astro05
#CheckUMAP(Astro_Seu)
Astro01_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 < -1 & Astro_UMAP$UMAP_2 > 2 | row.names(Astro_UMAP) %in% row.names(Astro01Clean))
Astro02_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 > 8 & Astro_UMAP$UMAP_2 > 2.5 | row.names(Astro_UMAP) %in% row.names(Astro02Clean))
Astro03_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 > 3 & Astro_UMAP$UMAP_2 < -4.5 | row.names(Astro_UMAP) %in% row.names(Astro03Clean))
Astro04_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 > 4 & Astro_UMAP$UMAP_2 > -4.5 & Astro_UMAP$UMAP_2 < 2.5 | row.names(Astro_UMAP) %in% row.names(Astro04Clean))
Astro05_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 < -9 & Astro_UMAP$UMAP_2 > -2 & Astro_UMAP$UMAP_2 < 1 | row.names(Astro_UMAP) %in% colnames(Astro05))
#& ! row.names(Astro_UMAP) %in% c(row.names(Astro02_Extras))
#CheckInput = Astro05_Extras
#CheckUMAP(Astro_Seu)
Astro_Assigns = GenerateMetaData_Barcodes(list("Astro_01" = Astro01_Extras, "Astro_02" = Astro02_Extras, "Astro_03" = Astro03_Extras, "Astro_04" = Astro04_Extras, "Astro_05" = Astro05_Extras, "Astro_06" = Astro06Clean))
Astro02_Extras_Pt2 = subset(Astro_Seu, cells = Astro_Assigns$Barcodes, invert=T)
CheckInput = Astro02_Extras_Pt2
CheckUMAP(Astro_Seu)
Astro_Assigns$Dups = duplicated(Astro_Assigns$Barcodes) | duplicated(Astro_Assigns$Barcodes, fromLast=T)
Astro_Assigns_T = subset(Astro_Assigns, Astro_Assigns$Dups == T)
unique(Astro_Assigns_T$Pop)
Astro_Assigns = GenerateMetaData(list( "Astro_01" = Astro01_Extras, "Astro_02" = Astro02_Extras, "Astro_03" = Astro03_Extras, "Astro_04" = Astro04_Extras, "Astro_05" = Astro05_Extras, "Astro_06" = Astro06Clean, "Astro_06" = Astro02_Extras_Pt2))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Endothelial cells
Idents(KaZhouAll) = "MainClusters"
Endo_Seu = subset(KaZhouAll, idents = "Endothelial")
DefaultAssay(Endo_Seu) = "integrated"
Endo_Seu = FindVariableFeatures(Endo_Seu)
Endo_Seu = ScaleData(Endo_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Endo_Seu) = "integrated"
Endo_Seu = RunPCA(Endo_Seu, npcs = 30)
#Endo_Seu <- RunHarmony(Endo_Seu, group.by.vars = "Timepoint2")
Endo_Seu <- RunUMAP(Endo_Seu, dims = 1:30, spread= 5)#, reduction = "harmony")
DefaultAssay(Endo_Seu) = "RNA"
#####
set.dim = c(20)
set.res = c(1)
set.kparam = c(20)
#ClusterFunc_All_RNA(Endo_Seu)
DefaultAssay(Endo_Seu) = "integrated"
Endo_Seu <- FindNeighbors(Endo_Seu, k.param=20, dims=1:20)
Endo_Seu <- FindClusters(Endo_Seu, resolution = 1)
DefaultAssay(Endo_Seu) = "RNA"
#CheckInput = Endo08
#CheckUMAP(Endo_Seu)
Endo_UMAP = as.data.frame(Endo_Seu@reductions$umap@cell.embeddings)
Endo01 = subset(Endo_Seu, idents = c(0))
Endo01Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo01) & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 > -15 & Endo_UMAP$UMAP_2 < 3)
Endo02 = subset(Endo_Seu, idents = c(1))
Endo02Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo02) & Endo_UMAP$UMAP_1 > -22 & Endo_UMAP$UMAP_1 < -2 & Endo_UMAP$UMAP_2 > -10 & Endo_UMAP$UMAP_2 < 12)
Endo03 = subset(Endo_Seu, idents = c(3,4,11,12))
Endo03Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo03)& Endo_UMAP$UMAP_1 > 18 & Endo_UMAP$UMAP_2 < -2 | row.names(Endo_UMAP) %in% colnames(Endo03)& Endo_UMAP$UMAP_1 > 25)
Endo04 = subset(Endo_Seu, idents = c(2))
Endo04Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo04) & Endo_UMAP$UMAP_1 < 0)
Endo05 = subset(Endo_Seu, idents = c(5))
Endo05Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo05) & Endo_UMAP$UMAP_2 > 11)
Endo06 = subset(Endo_Seu, idents = c(6,7))
Endo06Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo06) & Endo_UMAP$UMAP_1 > -2 & Endo_UMAP$UMAP_1 < 22)
Endo07 = subset(Endo_Seu, idents = c(8))
Endo07Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo07) & Endo_UMAP$UMAP_1 < -10)
Endo08 = subset(Endo_Seu, idents = c(9))
Endo08Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo08) & Endo_UMAP$UMAP_1 < 5)
Endo_REST = subset(Endo_Seu, cells = c(row.names(Endo01Clean), row.names(Endo02Clean), row.names(Endo03Clean), row.names(Endo04Clean), row.names(Endo05Clean), row.names(Endo06Clean), row.names(Endo07Clean), row.names(Endo08Clean)), invert=T)
#CheckInput = Endo06_Extras
#CheckUMAP(Endo_Seu)
Endo01_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 > -13 & Endo_UMAP$UMAP_2 < 0 | row.names(Endo_UMAP) %in% row.names(Endo01Clean))
Endo02_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > -21 & Endo_UMAP$UMAP_1 < -2 & Endo_UMAP$UMAP_2 > 0 & Endo_UMAP$UMAP_2 < 11 | row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < -18 & Endo_UMAP$UMAP_2 > 9 | row.names(Endo_UMAP) %in% row.names(Endo02Clean))
Endo03_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > 18 & Endo_UMAP$UMAP_2 < -2 | row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > 25 | row.names(Endo_UMAP) %in% row.names(Endo03Clean))
Endo04_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 < -13 | row.names(Endo_UMAP) %in% row.names(Endo04Clean))
Endo05_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > -18 & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 > 11 & Endo_UMAP$UMAP_2 < 23 | row.names(Endo_UMAP) %in% row.names(Endo05Clean))
Endo06_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > -1 & Endo_UMAP$UMAP_2 > -15 & Endo_UMAP$UMAP_2 < 23 & ! row.names(Endo_UMAP) %in% row.names(Endo03_Extras) | row.names(Endo_UMAP) %in% row.names(Endo06Clean))
Endo07_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < -20 & Endo_UMAP$UMAP_2 > 0 & Endo_UMAP$UMAP_2 < 9 | row.names(Endo_UMAP) %in% row.names(Endo07Clean))
Endo08_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < 10 & Endo_UMAP$UMAP_2 > 25 | row.names(Endo_UMAP) %in% row.names(Endo08Clean))
#CheckInput = Endo08_Extras
#CheckUMAP(Endo_Seu)
Endo_Assigns = GenerateMetaData_Barcodes(list("Endo_01" = Endo01_Extras, "Endo_02" = Endo02_Extras, "Endo_03" = Endo03_Extras, "Endo_04" = Endo04_Extras, "Endo_05" = Endo05_Extras, "Endo_06" = Endo06_Extras, "Endo_07" = Endo07_Extras, "Endo_08" = Endo08_Extras))
Endo02_Extras_Pt2 = subset(Endo_Seu, cells = Endo_Assigns$Barcodes, invert=T)
CheckInput = Endo02_Extras_Pt2
CheckUMAP(Endo_Seu)
Endo_Assigns$Dups = duplicated(Endo_Assigns$Barcodes) | duplicated(Endo_Assigns$Barcodes, fromLast=T)
Endo_Assigns_T = subset(Endo_Assigns, Endo_Assigns$Dups == T)
unique(Endo_Assigns_T$Pop)
Endo_Assigns = GenerateMetaData(list("Vein_01" = Endo01_Extras, "Capillary_01" = Endo02_Extras, "Artery_01" = Endo03_Extras, "Arteriole" = Endo04_Extras, "Capillary_02" = Endo05_Extras, "Artery_02" = Endo06_Extras, "Vein_02" = Endo07_Extras, "Venule" = Endo08_Extras))
#save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Tanycytes
Idents(KaZhouAll) = "MainClusters"
Tany_Seu = subset(KaZhouAll, idents = "Tanycytes")
DefaultAssay(Tany_Seu) = "integrated"
Tany_Seu = FindVariableFeatures(Tany_Seu)
Tany_Seu = ScaleData(Tany_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Tany_Seu) = "integrated"
Tany_Seu = RunPCA(Tany_Seu, npcs = 20)
#Tany_Seu <- RunHarmony(Tany_Seu, group.by.vars = "Timepoint2")
Tany_Seu <- RunUMAP(Tany_Seu, dims = 1:20, spread= 10)#, reduction = "harmony")
DefaultAssay(Tany_Seu) = "RNA"
#####
set.dim = c(20)
set.res = c(1)
set.kparam = c(15)
#ClusterFunc_All_RNA(Tany_Seu)
DefaultAssay(Tany_Seu) = "integrated"
Tany_Seu <- FindNeighbors(Tany_Seu, k.param=15, dims=1:20)
Tany_Seu <- FindClusters(Tany_Seu, resolution = 1)
DefaultAssay(Tany_Seu) = "RNA"
#CheckInput = Tany05Clean
#CheckUMAP(Tany_Seu)
Tany_UMAP = as.data.frame(Tany_Seu@reductions$umap@cell.embeddings)
Tany01 = subset(Tany_Seu, idents = c(5)) #Alpha 1
Tany01Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany01) & Tany_UMAP$UMAP_1 < 18 & Tany_UMAP$UMAP_2 > 25)
Tany02 = subset(Tany_Seu, idents = c(0,8)) #Alpha2
Tany02Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany02) & Tany_UMAP$UMAP_1 > -25 & Tany_UMAP$UMAP_1 < 10 & Tany_UMAP$UMAP_2 > -20 & Tany_UMAP$UMAP_2 < 20)
Tany03 = subset(Tany_Seu, idents = c(1)) #Alpha 2 (Pt2)
Tany03Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany03) & Tany_UMAP$UMAP_1 < 30 & Tany_UMAP$UMAP_2 > -10 & Tany_UMAP$UMAP_2 < 30)
Tany04 = subset(Tany_Seu, idents = c(9)) #Beta1
Tany04Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany04) & Tany_UMAP$UMAP_1 < -20 & Tany_UMAP$UMAP_2 > 0)
Tany05 = subset(Tany_Seu, idents = c(2,3)) #Beta2
Tany05Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany05) & Tany_UMAP$UMAP_1 > 25 & Tany_UMAP$UMAP_2 < 12 & Tany_UMAP$UMAP_2 > -18 | row.names(Tany_UMAP) %in% colnames(Tany05) & Tany_UMAP$UMAP_1 > 15 & Tany_UMAP$UMAP_2 < 0 & Tany_UMAP$UMAP_2 > -18)
Tany06 = subset(Tany_Seu, idents = c(4))
Tany06Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany06) & Tany_UMAP$UMAP_1 > -25 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > -40 & Tany_UMAP$UMAP_2 < -5)
Tany07 = subset(Tany_Seu, idents = c(6))
Tany07Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany07) & Tany_UMAP$UMAP_1 > -5 & Tany_UMAP$UMAP_2 > -31 & Tany_UMAP$UMAP_2 < -5)
Tany08 = subset(Tany_Seu, idents = c(7)) #Rax+
Tany08Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany08) & Tany_UMAP$UMAP_1 > -22 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > 10 & Tany_UMAP$UMAP_2 < 25)
Tany09 = subset(Tany_Seu, idents = c(10))
Tany09Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany09) & Tany_UMAP$UMAP_2 < -25)
Tany10 = subset(Tany_Seu, idents = c(11)) #Rax+
Tany10Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany10) & Tany_UMAP$UMAP_2 > -20 & Tany_UMAP$UMAP_2 < -2)
Tany11 = subset(Tany_Seu, idents = c(12))
Tany11Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany11) & Tany_UMAP$UMAP_1 < -25)
Tany12 = subset(Tany_Seu, idents = c(13)) #Rax+
#CheckInput = Tany05Clean
#CheckUMAP(Tany_Seu)
Tany_REST = subset(Tany_Seu, cells = c(row.names(Tany01Clean), row.names(Tany02Clean), row.names(Tany03Clean), row.names(Tany04Clean), row.names(Tany05Clean), row.names(Tany06Clean), row.names(Tany07Clean), row.names(Tany08Clean), row.names(Tany09Clean), row.names(Tany10Clean), row.names(Tany11Clean), colnames(Tany12)), invert=T)
#CheckInput = Tany07Clean
#CheckUMAP(Tany_Seu)
Tany01_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > 0 & Tany_UMAP$UMAP_2 > 30 | row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -20 & Tany_UMAP$UMAP_1 < 0 & Tany_UMAP$UMAP_2 > 26.5 | row.names(Tany_UMAP) %in% row.names(Tany01Clean))
Tany04_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -23 & Tany_UMAP$UMAP_2 > 5 & Tany_UMAP$UMAP_2 < 25 | row.names(Tany_UMAP) %in% row.names(Tany04Clean))
Tany06_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -25 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > -40 & Tany_UMAP$UMAP_2 < -5| row.names(Tany_UMAP) %in% row.names(Tany06Clean))
Tany09_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -12 & Tany_UMAP$UMAP_1 < 5 & Tany_UMAP$UMAP_2 < -30 & ! row.names(Tany_UMAP) %in% row.names(Tany06_Extras) | row.names(Tany_UMAP) %in% row.names(Tany09Clean))
Tany10_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -20 & Tany_UMAP$UMAP_2 > -12 & Tany_UMAP$UMAP_2 < 5 & ! row.names(Tany_UMAP) %in% c(row.names(Tany01_Extras), row.names(Tany04_Extras), row.names(Tany06_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany10Clean))
Tany11_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -25 & Tany_UMAP$UMAP_2 > -25 & Tany_UMAP$UMAP_2 < -12 | row.names(Tany_UMAP) %in% row.names(Tany11Clean))
Tany12_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -25 & Tany_UMAP$UMAP_2 > 25 | row.names(Tany_UMAP) %in% colnames(Tany12))
Tany02_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -27 & Tany_UMAP$UMAP_1 < 2 & Tany_UMAP$UMAP_2 > -15 & Tany_UMAP$UMAP_2 < 16 & ! row.names(Tany_UMAP) %in% c(row.names(Tany10_Extras), row.names(Tany04_Extras), row.names(Tany06_Extras), row.names(Tany11_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany02Clean))
Tany08_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -24 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > 5 & Tany_UMAP$UMAP_2 < 27 & ! row.names(Tany_UMAP) %in% c(row.names(Tany01_Extras), row.names(Tany02_Extras) ) | row.names(Tany_UMAP) %in% row.names(Tany08Clean))
Tany03_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -3 & Tany_UMAP$UMAP_1 < 33 & Tany_UMAP$UMAP_2 > -10 & Tany_UMAP$UMAP_2 < 30 & ! row.names(Tany_UMAP) %in% c(row.names(Tany01_Extras), row.names(Tany02_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany03Clean))
Tany05_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > 18 & Tany_UMAP$UMAP_2 > -22 & Tany_UMAP$UMAP_2 < 10 & ! row.names(Tany_UMAP) %in% row.names(Tany03_Extras) | row.names(Tany_UMAP) %in% row.names(Tany05Clean))
Tany07_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -5 & Tany_UMAP$UMAP_1 < 25 & Tany_UMAP$UMAP_2 > -31 & Tany_UMAP$UMAP_2 < -10 & ! row.names(Tany_UMAP) %in% c(row.names(Tany03_Extras), row.names(Tany02_Extras), row.names(Tany05_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany07Clean))
#& ! row.names(Tany_UMAP) %in% c(row.names(Tany02_Extras)) , row.names(?)
#CheckInput = Tany06_Extras
#CheckUMAP(Tany_Seu)
Tany_Assigns = GenerateMetaData_Barcodes(list("Alpha_01" = Tany01_Extras, "Alpha_02" = Tany02_Extras, "Alpha_03" = Tany03_Extras, "Beta_01" = Tany04_Extras, "Beta_02" = Tany05_Extras, "RG_16" = Tany06_Extras, "Alpha_04" = Tany07_Extras, "Tany_07" = Tany08_Extras, "RG_17" = Tany09_Extras, "Tany_08" = Tany10_Extras, "Alpha_05" = Tany11_Extras, "Alpha_05" = Tany12_Extras))
#Tany02_Extras_Pt2 = subset(Tany_Seu, cells = Tany_Assigns$Barcodes, invert=T)
CheckInput = Tany02_Extras_Pt2
CheckUMAP(Tany_Seu)
Tany_Assigns$Dups = duplicated(Tany_Assigns$Barcodes) | duplicated(Tany_Assigns$Barcodes, fromLast=T)
Tany_Assigns_T = subset(Tany_Assigns, Tany_Assigns$Dups == T)
unique(Tany_Assigns_T$Pop)
Tany_Assigns = GenerateMetaData(list("Alpha_01" = Tany01_Extras, "Alpha_02" = Tany02_Extras, "Alpha_03" = Tany03_Extras, "Beta_01" = Tany04_Extras, "Beta_02" = Tany05_Extras, "RG_16" = Tany06_Extras, "Alpha_04" = Tany07_Extras, "Tany_07" = Tany08_Extras, "RG_17" = Tany09_Extras, "Tany_08" = Tany10_Extras, "Alpha_05" = Tany11_Extras, "Alpha_05" = Tany12_Extras, "Alpha_02" =Tany02_Extras_Pt2))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Ependymocytes
Idents(KaZhouAll) = "MainClusters"
Ependy_Seu = subset(KaZhouAll, idents = "Ependymal")
DefaultAssay(Ependy_Seu) = "integrated"
Ependy_Seu = FindVariableFeatures(Ependy_Seu)
Ependy_Seu = ScaleData(Ependy_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Ependy_Seu) = "integrated"
Ependy_Seu = RunPCA(Ependy_Seu, npcs = 10)
#Ependy_Seu <- RunHarmony(Ependy_Seu, group.by.vars = "Timepoint2")
Ependy_Seu <- RunUMAP(Ependy_Seu, dims = 1:10, spread= 2)#, reduction = "harmony")
DefaultAssay(Ependy_Seu) = "RNA"
#####
set.dim = c(10)
set.res = c(1)
set.kparam = c(100)
ClusterFunc_All_RNA(Ependy_Seu)
DefaultAssay(Ependy_Seu) = "integrated"
Ependy_Seu <- FindNeighbors(Ependy_Seu, k.param=100, dims=1:10)
Ependy_Seu <- FindClusters(Ependy_Seu, resolution = 1)
DefaultAssay(Ependy_Seu) = "RNA"
CheckInput = Ependy01_Extras
CheckUMAP(Ependy_Seu)
Ependy_UMAP = as.data.frame(Ependy_Seu@reductions$umap@cell.embeddings)
Ependy01 = subset(Ependy_Seu, idents = c(5), invert=T)
Ependy01Clean = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy01) & Ependy_UMAP$UMAP_1 < 10)
Ependy02 = subset(Ependy_Seu, idents = c(5))
Ependy02Clean = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy02) & Ependy_UMAP$UMAP_1 > 10)
Ependy_REST = subset(Ependy_Seu, cells = c(row.names(Ependy01Clean), row.names(Ependy02Clean)), invert=T)
Ependy01_Extras = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy_REST) & Ependy_UMAP$UMAP_1 < 10 | row.names(Ependy_UMAP) %in% row.names(Ependy01Clean))
Ependy02_Extras = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy_REST) & Ependy_UMAP$UMAP_1 > 10 | row.names(Ependy_UMAP) %in% row.names(Ependy02Clean))
Ependy_Assigns = GenerateMetaData(list( "Ependy_01" = Ependy01_Extras, "Ependy_02" = Ependy02_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Oligodendrocytes
Idents(KaZhouAll) = "MainClusters"
Olig_Seu = subset(KaZhouAll, idents = c("Oligodendrocytes"))
#Olig_Assigns= subset(MainClusters, MainClusters$Pop %in% c("Oligodendrocytes [Dividing]", "Oligodendrocytes [Immature]", "Oligodendrocytes [Intermediate]", "Oligodendrocytes [Mature]"))
DefaultAssay(Olig_Seu) = "integrated"
Olig_Seu = FindVariableFeatures(Olig_Seu)
Olig_Seu = ScaleData(Olig_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(Olig_Seu) = "integrated"
Olig_Seu = RunPCA(Olig_Seu, npcs = 20)
Olig_Seu <- RunHarmony(Olig_Seu, group.by.vars = "Timepoint2")
Olig_Seu <- RunUMAP(Olig_Seu, dims = 1:20, spread= 2, reduction = "harmony")
DefaultAssay(Olig_Seu) = "RNA"
set.dim = c(20)
set.res = c(1)
set.kparam = c(20)
ClusterFunc_All_RNA(Olig_Seu)
DefaultAssay(Olig_Seu) = "integrated"
Olig_Seu <- FindNeighbors(Olig_Seu, k.param=20, dims=1:20)
Olig_Seu <- FindClusters(Olig_Seu, resolution = 1)
DefaultAssay(Olig_Seu) = "RNA"
CheckInput = Olig07Clean
CheckUMAP(Olig_Seu)
Olig_UMAP = as.data.frame(Olig_Seu@reductions$umap@cell.embeddings)
Olig01 = subset(Olig_Seu, idents = c(9)) #Dividing
Olig02 = subset(Olig_Seu, idents = c(13)) #Dividing
Olig02Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig02) & Olig_UMAP$UMAP_1 > 0 & Olig_UMAP$UMAP_1 < 5 & Olig_UMAP$UMAP_2 > 1)
Olig03 = subset(Olig_Seu, idents = c(0,1,5,6,8)) #Immature
Olig03Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig03) & Olig_UMAP$UMAP_1 > 0 & Olig_UMAP$UMAP_2 > -10 & Olig_UMAP$UMAP_2 < 2 | row.names(Olig_UMAP) %in% colnames(Olig03) & Olig_UMAP$UMAP_1 > -7 & Olig_UMAP$UMAP_2 > -7 & Olig_UMAP$UMAP_2 < -0.3)
Olig04 = subset(Olig_Seu, idents = c(2))#Immature
Olig04Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig04) & Olig_UMAP$UMAP_1 > 6 & Olig_UMAP$UMAP_2 > 1 & Olig_UMAP$UMAP_2 < 12)
Olig05 = subset(Olig_Seu, idents = c(10)) #Immature
Olig05Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig05) & Olig_UMAP$UMAP_1 > -4 & Olig_UMAP$UMAP_1 < 3 & Olig_UMAP$UMAP_2 > 0 & Olig_UMAP$UMAP_2 < 5 )
Olig06 = subset(Olig_Seu, idents = c(11)) #Immature
Olig06Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig06) & Olig_UMAP$UMAP_1 > -6 & Olig_UMAP$UMAP_1 < -1 & Olig_UMAP$UMAP_2 > -1 & Olig_UMAP$UMAP_2 < 3)
Olig07 = subset(Olig_Seu, idents = c(16)) #Immature
Olig07Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig07) & Olig_UMAP$UMAP_1 > -4 & Olig_UMAP$UMAP_1 < 3 & Olig_UMAP$UMAP_2> 9)
Olig08 = subset(Olig_Seu, idents = c(17)) #Immature
Olig08Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig08) & Olig_UMAP$UMAP_2 > 6 & Olig_UMAP$UMAP_2 < 9)
Olig09 = subset(Olig_Seu, idents = c(14)) #Immature
Olig10 = subset(Olig_Seu, idents = c(7,12)) #Intermediate
Olig10Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig10) & Olig_UMAP$UMAP_1 > -12& Olig_UMAP$UMAP_1 < -3 & Olig_UMAP$UMAP_2 < 2.5)
Olig11 = subset(Olig_Seu, idents = c(3,4,15)) #Mature
Olig11Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig11) & Olig_UMAP$UMAP_1 < -8)
Olig_REST = subset(Olig_Seu, cells = c(colnames(Olig01), row.names(Olig02Clean), row.names(Olig03Clean), row.names(Olig04Clean), row.names(Olig05Clean), row.names(Olig06Clean), row.names(Olig07Clean), row.names(Olig08Clean),colnames(Olig09), row.names(Olig10Clean),row.names(Olig11Clean)), invert=T)
CheckInput = Olig03_Extras
CheckUMAP(Olig_Seu)
Olig01_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 5 & Olig_UMAP$UMAP_2 > 10.5 | row.names(Olig_UMAP) %in% colnames(Olig01))
Olig02_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 2 & Olig_UMAP$UMAP_1 < 5 & Olig_UMAP$UMAP_2 > 2 | row.names(Olig_UMAP) %in% row.names(Olig02Clean))
Olig03_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 0 & Olig_UMAP$UMAP_2 > -10 & Olig_UMAP$UMAP_2 < 2 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -7 & Olig_UMAP$UMAP_2 > -7 & Olig_UMAP$UMAP_2 < -0.3 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -7 & Olig_UMAP$UMAP_2 > -10 & Olig_UMAP$UMAP_2 < -5 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 5 & Olig_UMAP$UMAP_2 < 3 | row.names(Olig_UMAP) %in% row.names(Olig03Clean))
Olig04_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 5 & Olig_UMAP$UMAP_2 > 3 & Olig_UMAP$UMAP_2 < 10.5| row.names(Olig_UMAP) %in% row.names(Olig04Clean))
Olig05_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST)& Olig_UMAP$UMAP_1 > -4 & Olig_UMAP$UMAP_1 < 3 & Olig_UMAP$UMAP_2 > 2 & Olig_UMAP$UMAP_2 < 5.5 | row.names(Olig_UMAP) %in% row.names(Olig05Clean))
Olig06_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST)& Olig_UMAP$UMAP_1 > -6 & Olig_UMAP$UMAP_1 < 0 & Olig_UMAP$UMAP_2 > -1 & Olig_UMAP$UMAP_2 < 3 & ! row.names(Olig_UMAP) %in% c(row.names(Olig05_Extras), row.names(Olig03_Extras)) | row.names(Olig_UMAP) %in% row.names(Olig06Clean))
##
Olig07_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -3 & Olig_UMAP$UMAP_1 < 2 & Olig_UMAP$UMAP_2 > 7 | row.names(Olig_UMAP) %in% row.names(Olig07Clean))
Olig08_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -6 & Olig_UMAP$UMAP_1 < -3 & Olig_UMAP$UMAP_2 > 4 & ! row.names(Olig_UMAP) %in% row.names(Olig05_Extras) | row.names(Olig_UMAP) %in% row.names(Olig08Clean))
Olig09_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST)& Olig_UMAP$UMAP_2 < -12 | row.names(Olig_UMAP) %in% colnames(Olig09))
Olig11_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 < -11 & Olig_UMAP$UMAP_2 > -3 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 < -9 & Olig_UMAP$UMAP_2 > 3 | row.names(Olig_UMAP) %in% row.names(Olig11Clean))
Olig10_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -11 & Olig_UMAP$UMAP_1 < -3 & Olig_UMAP$UMAP_2 < 4 & ! row.names(Olig_UMAP) %in% c(row.names(Olig11_Extras), row.names(Olig03_Extras), row.names(Olig06_Extras)) | row.names(Olig_UMAP) %in% row.names(Olig10Clean))
CheckInput = Olig03_Extras
CheckUMAP(Olig_Seu)
Olig_Assigns = GenerateMetaData_Barcodes(list("Dividing_01" = Olig01_Extras, "Dividing_02" = Olig02_Extras, "Immature_01" = Olig03_Extras, "Immature_02" = Olig04_Extras, "Immature_03" = Olig05_Extras, "Immature_04" = Olig06_Extras, "Immature_05" = Olig07_Extras, "Immature_06" = Olig08_Extras, "Immature_07" = Olig09_Extras, "Intermediate" = Olig10_Extras, "Mature" = Olig11_Extras))
Olig02_Extras_Pt2 = subset(Olig_Seu, cells = Olig_Assigns$Barcodes, invert=T)
CheckInput = Olig02_Extras_Pt2
CheckUMAP(Olig_Seu)
Olig_Assigns$Dups = duplicated(Olig_Assigns$Barcodes) | duplicated(Olig_Assigns$Barcodes, fromLast=T)
Olig_Assigns_T = subset(Olig_Assigns, Olig_Assigns$Dups == T)
unique(Olig_Assigns_T$Pop)
Olig_Assigns = GenerateMetaData(list("Dividing_01" = Olig01_Extras, "Dividing_02" = Olig02_Extras, "Immature_01" = Olig03_Extras, "Immature_02" = Olig04_Extras, "Immature_03" = Olig05_Extras, "Immature_04" = Olig06_Extras, "Immature_05" = Olig07_Extras, "Immature_06" = Olig08_Extras, "Immature_07" = Olig09_Extras, "Intermediate" = Olig10_Extras, "Mature" = Olig11_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Assigns", "Olig_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
-
Neuronal subclustering:
Read in NEURONAL data and iterate UMAPs
EdKaZhouHypoNeurons = readRDS("~/Downloads/EdKaZhouHypoNeurons_mt10_integrated.rds")
set.seed(100)
EdKaZhouHypoNeurons@meta.data$SampleBroad = gsub("_.*", "", gsub("22T", "22", gsub("CS22", "GW10", EdKaZhouHypoNeurons@meta.data$sample)))
EdKaZhouHypoNeurons@meta.data$SampleBroad = ifelse(EdKaZhouHypoNeurons@meta.data$SampleBroad %in% c("10X190", "10X192", "10X193", "10X203", "10X360", "10X362", "10X376", "10X380", "10X389", "10X392"), EdKaZhouHypoNeurons@meta.data$Roi, EdKaZhouHypoNeurons@meta.data$SampleBroad)
EdKaZhouHypoNeurons@meta.data$SampleBroad = gsub("\\<Human HTHma\\>", "Adult_MN", gsub("\\<Human HTHma-HTHtub\\>", "Adult_TUB/MN", gsub("\\<Human HTHpo\\>", "Adult_PO", gsub("\\<Human HTHpo-HTHso\\>", "Adult_PO/SO", gsub("\\<Human HTHso\\>", "Adult_SO", gsub("\\<Human HTHso-HTHtub\\>", "Adult_SO/TUB", gsub("\\<Human HTHtub\\>", "Adult_TUB", gsub("\\<Human MN\\>", "Adult_MN", EdKaZhouHypoNeurons@meta.data$SampleBroad))))))))
EdKaZhouHypoNeurons@meta.data$SampleAdult = gsub("_.*", "", EdKaZhouHypoNeurons@meta.data$SampleBroad)
MainAssign = read.csv("~/Dropbox/LabMac/Fig2_Trajectories_GLOTMP_17JAN_20_20_3_k100AssignsByBarc.csv", na.strings=c("","Unassigned"))
#, "ARC", "TM", "DMH", "AH", "SCN", "PVH", "SMN", "VMH", "PO", "LH"
for(Pops in c("NA", "MN")){
GetBarcs = subset(MainAssign, MainAssign$Nuclei == Pops)
SubsetSeu = subset(EdKaZhouHypoNeurons, cells = GetBarcs$Barcs)
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = FindVariableFeatures(SubsetSeu)
SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DPlist2 = list()
DPlist = list()
FPlist = list()
Filename = paste(Pops, "28MAR23", sep="")
for(y in seq(10,50,10)){
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = RunPCA(SubsetSeu, npcs = y)
for(z in seq(10,y,10)){
for(s in c(2,5,10)){
SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s)
DefaultAssay(SubsetSeu) = "RNA"
FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, CompileNeurons, reduction="umap")
DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "SampleAdult") + labs(title = paste("PCA", y, "_dims", z, "_spread", s))
DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "SampleAdult", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s))
}}}
pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=75/1.3)
print(FPlist)
dev.off()
pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5)
print(DPlist)
dev.off()
pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5)
print(DPlist2)
dev.off()
}
for(Pops in c("VMH", "LH")){
GetBarcs = subset(MainAssign, MainAssign$Nuclei == Pops)
SubsetSeu = subset(EdKaZhouHypoNeurons, cells = GetBarcs$Barcs)
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = FindVariableFeatures(SubsetSeu)
SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c( "percent.mt", "nFeature_RNA"), verbose = F)
DPlist2 = list()
DPlist = list()
FPlist = list()
Filename = paste(Pops, "28MAR23_Harmony", sep="")
for(y in seq(10,50,10)){
DefaultAssay(SubsetSeu) = "integrated"
SubsetSeu = RunPCA(SubsetSeu, npcs = y)
SubsetSeu <- RunHarmony(SubsetSeu, group.by.vars = "SampleAdult")
for(z in seq(10,y,10)){
for(s in c(2,5,10)){
SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s, reduction="harmony")
DefaultAssay(SubsetSeu) = "RNA"
FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, CompileNeurons, reduction="umap")
DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "SampleAdult") + labs(title = paste("PCA", y, "_dims", z, "_spread", s))
DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "SampleAdult", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s))
}}}
pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=75/1.3)
print(FPlist)
dev.off()
pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5)
print(DPlist)
dev.off()
pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5)
print(DPlist2)
dev.off()
}
ARC
GetARCBarcs = subset(MainAssign, MainAssign$Nuclei == "ARC")
ARC_Seu = subset(EdKaZhouHypoNeurons, cells = GetARCBarcs$Barcs)
DefaultAssay(ARC_Seu) = "integrated"
ARC_Seu = FindVariableFeatures(ARC_Seu)
ARC_Seu = ScaleData(ARC_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(ARC_Seu) = "integrated"
ARC_Seu = RunPCA(ARC_Seu, npcs = 20)
ARC_Seu <- RunUMAP(ARC_Seu, dims = 1:20, spread= 2)
DefaultAssay(ARC_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(50)
##ClusterFunc_All_RNA(ARC_Seu)
DefaultAssay(ARC_Seu) = "integrated"
ARC_Seu <- FindNeighbors(ARC_Seu, k.param=50, dims=1:20)
ARC_Seu <- FindClusters(ARC_Seu, resolution = 1)
DefaultAssay(ARC_Seu) = "RNA"
ARC_UMAP = as.data.frame(ARC_Seu@reductions$umap@cell.embeddings)
ARC01 = subset(ARC_Seu, idents = 0)
ARC01Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC01) & ARC_UMAP$UMAP_1 < -3 & ARC_UMAP$UMAP_2 > 5)
ARC02 = subset(ARC_Seu, idents = 1)
ARC02Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC02) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_2 > -9 & ARC_UMAP$UMAP_2 < 4)
TM01 = subset(ARC_Seu, idents = c(2,3))
TM01Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(TM01) & ARC_UMAP$UMAP_1 > -3 & ARC_UMAP$UMAP_2 > 7)
ARC03 = subset(ARC_Seu, idents = 4)
ARC03Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC03) & ARC_UMAP$UMAP_1 < 2 & ARC_UMAP$UMAP_2 < -11)
ARC04 = subset(ARC_Seu, idents = 5)
ARC04Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC04) & ARC_UMAP$UMAP_1 < -7 & ARC_UMAP$UMAP_2 < 5)
ARC05 = subset(ARC_Seu, idents = 6)
ARC06 = subset(ARC_Seu, idents = 7)
ARC06Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC06) & ARC_UMAP$UMAP_1 > 10 & ARC_UMAP$UMAP_2 < -0.5)
ARC07 = subset(ARC_Seu, idents = c(8, 18))
ARC07Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC07) & ARC_UMAP$UMAP_1 < 0 & ARC_UMAP$UMAP_2 < 8)
ARC08 = subset(ARC_Seu, idents = 9)
ARC08Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC08) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_2 < -5)
ARC09 = subset(ARC_Seu, idents = 10)
ARC09Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC09) & ARC_UMAP$UMAP_1 > 0)
ARC10 = subset(ARC_Seu, idents = 11)
ARC11 = subset(ARC_Seu, idents = 12)
ARC11Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC11) & ARC_UMAP$UMAP_1 > -2 & ARC_UMAP$UMAP_1 < 3)
ARC12 = subset(ARC_Seu, idents = 13)
ARC13 = subset(ARC_Seu, idents = 14)
ARC14 = subset(ARC_Seu, idents = 15)
ARC15 = subset(ARC_Seu, idents = 16)
ARC16 = subset(ARC_Seu, idents = 17)
ARC17 = subset(ARC_Seu, idents = 19)
ARC_REST = subset(ARC_Seu, cells = c(colnames(ARC05), colnames(ARC10), colnames(ARC12), colnames(ARC13), colnames(ARC14), colnames(ARC15), colnames(ARC16), colnames(ARC17), row.names(ARC01Clean), row.names(ARC02Clean), row.names(ARC03Clean), row.names(ARC04Clean), row.names(ARC06Clean), row.names(ARC07Clean), row.names(ARC08Clean),row.names(ARC09Clean), row.names(ARC11Clean), row.names(TM01Clean)), invert=T)
#CheckInput = ARC_REST
#CheckUMAP(ARC_Seu)
ARC01_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < -5 & ARC_UMAP$UMAP_2 > 4 | row.names(ARC_UMAP) %in% row.names(ARC01Clean))
ARC02_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 4 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 > -3 & ARC_UMAP$UMAP_2 < 4 | row.names(ARC_UMAP) %in% row.names(ARC02Clean))
TM01_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > -3 & ARC_UMAP$UMAP_2 > 8 | row.names(ARC_UMAP) %in% row.names(TM01Clean))
ARC03_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < 0 & ARC_UMAP$UMAP_2 < -11 | row.names(ARC_UMAP) %in% row.names(ARC03Clean))
ARC04_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < -10 & ARC_UMAP$UMAP_2 < 5 & ! row.names(ARC_UMAP) %in% row.names(ARC01_Extras) | row.names(ARC_UMAP) %in% row.names(ARC04Clean))
ARC05_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_2 < -13 | row.names(ARC_UMAP) %in% colnames(ARC05))
#ARC06_Extras #NONE
ARC08_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% row.names(ARC05_Extras) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_1 < 4 & ARC_UMAP$UMAP_2 < -6 | row.names(ARC_UMAP) %in% row.names(ARC08Clean))
ARC09_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC02_Extras), row.names(ARC08_Extras)) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 > -10 & ARC_UMAP$UMAP_2 < -1 | row.names(ARC_UMAP) %in% row.names(ARC09Clean))
ARC10_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < -10 & ARC_UMAP$UMAP_2 < -4 & ! row.names(ARC_UMAP) %in% row.names(ARC04_Extras) | row.names(ARC_UMAP) %in% colnames(ARC10))
ARC11_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > -2 & ARC_UMAP$UMAP_1 < 2 & ARC_UMAP$UMAP_2 > -2.5 & ARC_UMAP$UMAP_2 < 2 | row.names(ARC_UMAP) %in% row.names(ARC11Clean))
#ARC12_Extras #NONE
ARC13_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC08_Extras),row.names(ARC09_Extras), row.names(ARC05_Extras)) & ARC_UMAP$UMAP_1 > 3 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 < -6 | row.names(ARC_UMAP) %in% colnames(ARC13))
ARC14_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > -1 & ARC_UMAP$UMAP_1 < 5 & ARC_UMAP$UMAP_2 > 0 & ARC_UMAP$UMAP_2 < 9 & ! row.names(ARC_UMAP) %in% c(row.names(TM01_Extras), row.names(ARC11_Extras)) | row.names(ARC_UMAP) %in% colnames(ARC14))
ARC15_Extras = subset(ARC_UMAP,row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 5 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 > 2 & ARC_UMAP$UMAP_2 < 10 & ! row.names(ARC_UMAP) %in% c(row.names(TM01_Extras), row.names(ARC02_Extras)) | row.names(ARC_UMAP) %in% colnames(ARC15))
ARC16_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 10 | row.names(ARC_UMAP) %in% colnames(ARC16))
ARC17_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC08_Extras),row.names(ARC09_Extras), row.names(ARC05_Extras)) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_1 < 3 & ARC_UMAP$UMAP_2 > -8 & ARC_UMAP$UMAP_2 < -4 | row.names(ARC_UMAP) %in% colnames(ARC17))
ARC07_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC01_Extras), row.names(ARC11_Extras), row.names(TM01_Extras)) & ARC_UMAP$UMAP_1 > -10 & ARC_UMAP$UMAP_1 < 0 & ARC_UMAP$UMAP_2 > -5 | row.names(ARC_UMAP) %in% row.names(ARC07Clean))
#CheckInput = ARC10_Extras
#CheckUMAP(ARC_Seu)
set.dim = 20
set.res = 1
set.kparam = c(20)
#ClusterFunc_All_RNA(ARC10_Seu)
ARC07_Seu = subset(ARC_Seu, cells = row.names(ARC07_Extras))
#ClusterFunc_All_RNA(ARC07_Seu)
ARC10_Seu = subset(ARC_Seu, cells = row.names(ARC10_Extras))
#ClusterFunc_All_RNA(ARC10_Seu)
ARC14_Seu = subset(ARC_Seu, cells = row.names(ARC14_Extras))
#ClusterFunc_All_RNA(ARC14_Seu)
DefaultAssay(ARC07_Seu) = "integrated"
ARC07_Seu <- FindNeighbors(ARC07_Seu, k.param=5, dims=1:20)
ARC07_Seu <- FindClusters(ARC07_Seu, resolution = 1)
ARC_18 = subset(ARC07_Seu, idents = c(3,7))
ARC_19 = subset(ARC07_Seu, idents = c(0,2))
ARC_07_New = subset(ARC07_Seu, idents = c(3,7,0,2), invert=T)
DefaultAssay(ARC10_Seu) = "integrated"
ARC10_Seu <- FindNeighbors(ARC10_Seu, k.param=10, dims=1:20)
ARC10_Seu <- FindClusters(ARC10_Seu, resolution = 1)
ARC_20 = subset(ARC10_Seu, idents = c(1,3))
ARC_10_New = subset(ARC10_Seu, idents = c(1,3), invert=T)
DefaultAssay(ARC14_Seu) = "integrated"
ARC14_Seu <- FindNeighbors(ARC14_Seu, k.param=20, dims=1:20)
ARC14_Seu <- FindClusters(ARC14_Seu, resolution = 1)
ARC_21 = subset(ARC14_Seu, idents = c(0,1,5))
ARC_14_New = subset(ARC14_Seu, idents = c(0,1,5), invert=T)
###
ARC_REST = subset(ARC_Seu, cells = c(colnames(ARC05), colnames(ARC10), colnames(ARC12), colnames(ARC13), colnames(ARC14), colnames(ARC15), colnames(ARC16), colnames(ARC17), row.names(ARC01Clean), row.names(ARC02Clean), row.names(ARC03Clean), row.names(ARC04Clean), row.names(ARC06Clean), row.names(ARC07Clean), row.names(ARC08Clean),row.names(ARC09Clean), row.names(ARC11Clean), row.names(TM01Clean)), invert=T)
ARC_Assigns = GenerateMetaData(list("ARC_01" = ARC01_Extras, "ARC_02" = ARC02_Extras, "ARC_03" = ARC03_Extras, "ARC_04" = ARC04_Extras, "ARC_05" = ARC05_Extras, "ARC_07" = ARC_07_New,"ARC_08" = ARC08_Extras, "ARC_09" = ARC09_Extras, "ARC_10" = ARC_10_New, "ARC_11" = ARC11_Extras,"ARC_13" = ARC13_Extras,"ARC_14" = ARC_14_New,"ARC_15" = ARC15_Extras, "ARC_16" = ARC16_Extras,"ARC_17" = ARC17_Extras,"TM_01" = TM01_Extras, "ARC_06" = ARC06Clean,"ARC_12" = ARC12, "ARC_18" = ARC_18, "ARC_19" = ARC_19, "ARC_20" = ARC_20, "ARC_21" = ARC_21))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
PVH
GetPVHBarcs = subset(MainAssign, MainAssign$Nuclei == "PVH")
PVH_Seu = subset(EdKaZhouHypoNeurons, cells = GetPVHBarcs$Barcs)
DefaultAssay(PVH_Seu) = "integrated"
PVH_Seu = FindVariableFeatures(PVH_Seu)
PVH_Seu = ScaleData(PVH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(PVH_Seu) = "integrated"
PVH_Seu = RunPCA(PVH_Seu, npcs = 20)
PVH_Seu <- RunUMAP(PVH_Seu, dims = 1:20, spread= 5)
DefaultAssay(PVH_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(50)
##ClusterFunc_All_RNA(PVH_Seu)
DefaultAssay(PVH_Seu) = "integrated"
PVH_Seu <- FindNeighbors(PVH_Seu, k.param=50, dims=1:20)
PVH_Seu <- FindClusters(PVH_Seu, resolution = 1)
DefaultAssay(PVH_Seu) = "RNA"
PVH_UMAP = as.data.frame(PVH_Seu@reductions$umap@cell.embeddings)
PVH01 = subset(PVH_Seu, idents = 0)
PVH01Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH01) & PVH_UMAP$UMAP_1 > -12.5 & PVH_UMAP$UMAP_1 < 4 & PVH_UMAP$UMAP_2 > -10 & PVH_UMAP$UMAP_2 < 8)
PVH02 = subset(PVH_Seu, idents = 1)
PVH02Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH02) & PVH_UMAP$UMAP_1 > -10 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 12)
PVH03 = subset(PVH_Seu, idents = 2)
PVH03Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH03) & PVH_UMAP$UMAP_1 > 10 & PVH_UMAP$UMAP_2 > 10)
PVH04 = subset(PVH_Seu, idents = 3)
PVH04Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH04) & PVH_UMAP$UMAP_1 < -20)
PVH05 = subset(PVH_Seu, idents = 4)
PVH05Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH05) & PVH_UMAP$UMAP_1 < 25 & PVH_UMAP$UMAP_1 > 13 & PVH_UMAP$UMAP_2 < -10)
PVH06 = subset(PVH_Seu, idents = 5)
PVH06Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH06) & PVH_UMAP$UMAP_1 < 3 & PVH_UMAP$UMAP_1 > -10 & PVH_UMAP$UMAP_2 < 3 & PVH_UMAP$UMAP_2 > -10)
PVH07 = subset(PVH_Seu, idents = 6)
PVH08 = subset(PVH_Seu, idents = 7)
PVH09 = subset(PVH_Seu, idents = 8)
PVH09Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH09) & PVH_UMAP$UMAP_1 < -10 & PVH_UMAP$UMAP_1 > -20 & PVH_UMAP$UMAP_2 > -10)
PVH10 = subset(PVH_Seu, idents = 9)
PVH10Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH10) & PVH_UMAP$UMAP_2 < 10)
PVH11 = subset(PVH_Seu, idents = 10)
PVH11Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH11) & PVH_UMAP$UMAP_1 > 20)
PVH12 = subset(PVH_Seu, idents = 11)
PVH12Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH12) & PVH_UMAP$UMAP_1 < 10)
PVH13 = subset(PVH_Seu, idents = 12)
PVH14 = subset(PVH_Seu, idents = 13)
PVH14Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH14) & PVH_UMAP$UMAP_2 > -20)
PVH15 = subset(PVH_Seu, idents = 14)
PVH16 = subset(PVH_Seu, idents = c(15, 16))
#CheckInput = PVH17
#CheckUMAP(PVH_Seu)
PVH_REST = subset(PVH_Seu, cells = c(colnames(PVH07), colnames(PVH08), colnames(PVH13), colnames(PVH15), colnames(PVH16), row.names(PVH01Clean), row.names(PVH02Clean), row.names(PVH03Clean), row.names(PVH04Clean), row.names(PVH05Clean),row.names(PVH06Clean), row.names(PVH09Clean),row.names(PVH10Clean), row.names(PVH11Clean), row.names(PVH12Clean), row.names(PVH14Clean)), invert=T)
#CheckInput = PVH_REST
#CheckUMAP(PVH_Seu)
PVH01_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > -12.5 & PVH_UMAP$UMAP_1 < 0 & PVH_UMAP$UMAP_2 > -10 & PVH_UMAP$UMAP_2 < 8 | row.names(PVH_UMAP) %in% row.names(PVH01Clean))
PVH02_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > -3 & PVH_UMAP$UMAP_1 < 10 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 14 & ! row.names(PVH_UMAP) %in% row.names(PVH01_Extras) | row.names(PVH_UMAP) %in% row.names(PVH02Clean) | row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > -3 & PVH_UMAP$UMAP_1 < 18 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 12 & ! row.names(PVH_UMAP) %in% row.names(PVH01_Extras)| row.names(PVH_UMAP) %in% row.names(PVH02Clean))
PVH03_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > 10 & PVH_UMAP$UMAP_2 > 10| row.names(PVH_UMAP) %in% row.names(PVH03Clean))
PVH04_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -22 & PVH_UMAP$UMAP_2 > -5 & PVH_UMAP$UMAP_2 < 12| row.names(PVH_UMAP) %in% row.names(PVH04Clean))
PVH05_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 25 & PVH_UMAP$UMAP_1 > 13 & PVH_UMAP$UMAP_2 < -10| row.names(PVH_UMAP) %in% row.names(PVH05Clean))
#PVH06_Extras #None
PVH07_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 0 & PVH_UMAP$UMAP_1 > -15 & PVH_UMAP$UMAP_2 < -10| row.names(PVH_UMAP) %in% colnames(PVH07))
PVH08_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > 20 & PVH_UMAP$UMAP_2 < -10 & ! row.names(PVH_UMAP) %in% row.names(PVH05_Extras) | row.names(PVH_UMAP) %in% colnames(PVH08))
PVH09_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -10 & PVH_UMAP$UMAP_1 > -22 & PVH_UMAP$UMAP_2 < 5 & PVH_UMAP$UMAP_2 > -5| row.names(PVH_UMAP) %in% row.names(PVH09Clean))
PVH10_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_2 > -10 & PVH_UMAP$UMAP_2 < 0 & PVH_UMAP$UMAP_1 > 0 & PVH_UMAP$UMAP_1 < 10| row.names(PVH_UMAP) %in% row.names(PVH10Clean))
PVH11_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > 20 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 10| row.names(PVH_UMAP) %in% row.names(PVH11Clean))
PVH12_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 15 & PVH_UMAP$UMAP_1 > 0 & PVH_UMAP$UMAP_2 > 15| row.names(PVH_UMAP) %in% row.names(PVH12Clean))
PVH13_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 18 & PVH_UMAP$UMAP_1 > 0 & PVH_UMAP$UMAP_2 < -15 & ! row.names(PVH_UMAP) %in% c(row.names(PVH05_Extras), row.names(PVH08_Extras)) | row.names(PVH_UMAP) %in% colnames(PVH13))
PVH14_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -15 & PVH_UMAP$UMAP_2 > -20 & PVH_UMAP$UMAP_2 < -5| row.names(PVH_UMAP) %in% row.names(PVH14Clean))
PVH15_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -15 & PVH_UMAP$UMAP_2 < -20| row.names(PVH_UMAP) %in% colnames(PVH15))
PVH16_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & ! row.names(PVH_UMAP) %in% c(row.names(PVH01_Extras), row.names(PVH02_Extras), row.names(PVH03_Extras), row.names(PVH04_Extras), row.names(PVH05_Extras), row.names(PVH07_Extras), row.names(PVH08_Extras), row.names(PVH09_Extras), row.names(PVH10_Extras), row.names(PVH11_Extras), row.names(PVH12_Extras), row.names(PVH13_Extras), row.names(PVH14_Extras), row.names(PVH15_Extras))| row.names(PVH_UMAP) %in% colnames(PVH16))
set.dim = 20
set.res = 1
set.kparam = c(60)
PVH16_Seu = subset(PVH_Seu, cells = row.names(PVH16_Extras))
#ClusterFunc_All_RNA(PVH16_Seu)
PVH02_Seu = subset(PVH_Seu, cells = row.names(PVH02_Extras))
#ClusterFunc_All_RNA(PVH02_Seu)
DefaultAssay(PVH02_Seu) = "integrated"
PVH02_Seu <- FindNeighbors(PVH02_Seu, k.param=60, dims=1:20)
PVH02_Seu <- FindClusters(PVH02_Seu, resolution = 1)
PVH_17 = subset(PVH02_Seu, idents = c(1))
PVH_17Extra = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH02_Seu) & PVH_UMAP$UMAP_1 > 8 & PVH_UMAP$UMAP_2 < 7.5)
PVH_17Final = subset(PVH02_Seu, cells = unique(c(colnames(PVH_17), row.names(PVH_17Extra))))
PVH_02_New = subset(PVH02_Seu, cells = unique(c(colnames(PVH_17), row.names(PVH_17Extra))), invert=T)
DefaultAssay(PVH16_Seu) = "integrated"
PVH16_Seu <- FindNeighbors(PVH16_Seu, k.param=10, dims=1:20)
PVH16_Seu <- FindClusters(PVH16_Seu, resolution = 1)
PVH_18 = subset(PVH16_Seu, idents = c(2,4,8))
PVH_16_New = subset(PVH16_Seu, idents = c(2,4,8), invert=T)
PVH_Assigns = GenerateMetaData(list("PVH_01" = PVH01_Extras, "PVH_02" = PVH_02_New, "PVH_03" = PVH03_Extras, "PVH_04" = PVH04_Extras, "PVH_05" = PVH05_Extras, "PVH_06" = PVH06Clean, "PVH_07" = PVH07_Extras,"PVH_08" = PVH08_Extras, "PVH_09" = PVH09_Extras, "PVH_10" = PVH10_Extras, "PVH_11" = PVH11_Extras, "PVH_12" = PVH12_Extras,"PVH_13" = PVH13_Extras, "PVH_14" = PVH14_Extras, "PVH_15" = PVH15_Extras, "PVH_16" = PVH_16_New, "PVH_17" = PVH_17Final, "PVH_18" = PVH_18))
#PVH_REST2 = subset(PVH_Seu, cells = PVH_Assigns$Barcodes, invert=T)
#CheckInput = PVH_REST
#CheckUMAP(PVH_Seu)
#load("~/Hypothalamus_Subclustering_2023.RData")
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
TM
GetTMBarcs = subset(MainAssign, MainAssign$Nuclei == "TM")
TM_Seu = subset(EdKaZhouHypoNeurons, cells = GetTMBarcs$Barcs)
DefaultAssay(TM_Seu) = "integrated"
TM_Seu = FindVariableFeatures(TM_Seu)
TM_Seu = ScaleData(TM_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(TM_Seu) = "integrated"
TM_Seu = RunPCA(TM_Seu, npcs = 30)
TM_Seu <- RunHarmony(TM_Seu, group.by.vars = "SampleAdult")
TM_Seu <- RunUMAP(TM_Seu, dims = 1:30, spread= 2, reduction = "harmony")
DefaultAssay(TM_Seu) = "RNA"
set.dim = 30
set.res = 1
set.kparam = c(200)
##ClusterFunc_All_RNA(TM_Seu)
DefaultAssay(TM_Seu) = "integrated"
TM_Seu <- FindNeighbors(TM_Seu, k.param=100, dims=1:30)
TM_Seu <- FindClusters(TM_Seu, resolution = 1)
DefaultAssay(TM_Seu) = "RNA"
TM_UMAP = as.data.frame(TM_Seu@reductions$umap@cell.embeddings)
TM02 = subset(TM_Seu, idents = c(0,4))
TM02Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM02) & TM_UMAP$UMAP_1 < -3 & TM_UMAP$UMAP_2 < 3.5)
TM03 = subset(TM_Seu, idents = c(1,6))
TM03Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM03) & TM_UMAP$UMAP_1 > 0 & TM_UMAP$UMAP_1 < 13.5 & TM_UMAP$UMAP_2 > -3 )
TM04 = subset(TM_Seu, idents = c(5))
TM04Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM04) & TM_UMAP$UMAP_1 > -8 & TM_UMAP$UMAP_1 < 3 & TM_UMAP$UMAP_2 > -1)
TM05 = subset(TM_Seu, idents = 3)
TM05Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM05) & TM_UMAP$UMAP_1 > 13.5 | row.names(TM_UMAP) %in% colnames(TM05) & TM_UMAP$UMAP_1 > 10 & TM_UMAP$UMAP_2 < -2 )
#CheckInput = TM05
#CheckUMAP(TM_Seu)
TM_REST = subset(TM_Seu, cells = c(row.names(TM02Clean), row.names(TM03Clean), row.names(TM04Clean), row.names(TM05Clean)), invert=T)
#CheckInput = TM03_Extras
#CheckUMAP(TM_Seu)
TM02_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 < -3 & TM_UMAP$UMAP_2 < 3.5 | row.names(TM_UMAP) %in% row.names(TM02Clean))
TM03_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > 2 & TM_UMAP$UMAP_1 < 13.7 & TM_UMAP$UMAP_2 > -3 | row.names(TM_UMAP) %in% row.names(TM03Clean))
TM04_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > -10 & TM_UMAP$UMAP_1 < 5 & ! row.names(TM_UMAP) %in% c(row.names(TM02_Extras), row.names(TM03_Extras)) | row.names(TM_UMAP) %in% row.names(TM04Clean))
TM05_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > 2 & TM_UMAP$UMAP_1 > 13.7 | row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > 5 & TM_UMAP$UMAP_2 < -2 | row.names(TM_UMAP) %in% row.names(TM05Clean))
TM_Assigns = GenerateMetaData_Barcodes(list( "TM_02" = TM02_Extras, "TM_03" = TM03_Extras, "TM_04" = TM04_Extras,"TM_05" = TM05_Extras))
#TM_01_Rest = subset(TM_Seu, cells = TM_Assigns$Barcodes, invert=T)
#CheckInput = TM_01_Rest
#CheckUMAP(TM_Seu)
TM_Assigns$Dups = duplicated(TM_Assigns$Barcodes) | duplicated(TM_Assigns$Barcodes, fromLast=T)
TM_Assigns_T = subset(TM_Assigns, TM_Assigns$Dups == T)
unique(TM_Assigns_T$Pop)
TM_Assigns = GenerateMetaData(list("TM_02" = TM02_Extras, "TM_03" = TM03_Extras, "TM_04" = TM04_Extras,"TM_05" = TM05_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
VMH
GetVMHBarcs = subset(MainAssign, MainAssign$Nuclei == "VMH")
VMH_Seu = subset(EdKaZhouHypoNeurons, cells = GetVMHBarcs$Barcs)
DefaultAssay(VMH_Seu) = "integrated"
VMH_Seu = FindVariableFeatures(VMH_Seu)
VMH_Seu = ScaleData(VMH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(VMH_Seu) = "integrated"
VMH_Seu = RunPCA(VMH_Seu, npcs = 20)
VMH_Seu <- RunHarmony(VMH_Seu, group.by.vars = "SampleAdult")
VMH_Seu <- RunUMAP(VMH_Seu, dims = 1:20, spread= 5, reduction = "harmony")
DefaultAssay(VMH_Seu) = "RNA"
set.dim = c(20)
set.res = 1
set.kparam = c(50)
#ClusterFunc_All_RNA(VMH_Seu)
DefaultAssay(VMH_Seu) = "integrated"
VMH_Seu <- FindNeighbors(VMH_Seu, k.param=50, dims=1:20)
VMH_Seu <- FindClusters(VMH_Seu, resolution = 1)
DefaultAssay(VMH_Seu) = "RNA"
VMH_UMAP = as.data.frame(VMH_Seu@reductions$umap@cell.embeddings)
VMH01 = subset(VMH_Seu, idents = c(0))
VMH01Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH01) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > -8 & VMH_UMAP$UMAP_2 < 16)
VMH02 = subset(VMH_Seu, idents = 2)
VMH02Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH02) & VMH_UMAP$UMAP_1 > 2 & VMH_UMAP$UMAP_2 > 17 | row.names(VMH_UMAP) %in% colnames(VMH02) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > 15)
VMH03 = subset(VMH_Seu, idents = 3)
VMH03Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH03) & VMH_UMAP$UMAP_2 < -25)
VMH04 = subset(VMH_Seu, idents = 4)
VMH04Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH04)& VMH_UMAP$UMAP_1 > -15 & VMH_UMAP$UMAP_1 < -1 & VMH_UMAP$UMAP_2 > -11)
VMH05 = subset(VMH_Seu, idents = c(1,5))
VMH05Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH05) & VMH_UMAP$UMAP_1 > -7 & VMH_UMAP$UMAP_1 < 4 & VMH_UMAP$UMAP_2 > -1 & VMH_UMAP$UMAP_2 < 11 | row.names(VMH_UMAP) %in% colnames(VMH05) & VMH_UMAP$UMAP_1 > -2 & VMH_UMAP$UMAP_1 < 7 & VMH_UMAP$UMAP_2 > -5 & VMH_UMAP$UMAP_2 < 0)
VMH06Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH05)& VMH_UMAP$UMAP_1 > 4 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > -2 & VMH_UMAP$UMAP_2 < 20 | row.names(VMH_UMAP) %in% colnames(VMH05)& VMH_UMAP$UMAP_1 > 0 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > 11 & VMH_UMAP$UMAP_2 < 20 )
VMH07 = subset(VMH_Seu, idents = 6)
VMH07Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH07)& VMH_UMAP$UMAP_1 < -7)
VMH08 = subset(VMH_Seu, idents = c(7))
VMH08Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH08) & VMH_UMAP$UMAP_1 > -23 & VMH_UMAP$UMAP_1 < -10 & VMH_UMAP$UMAP_2 > 10)
VMH09 = subset(VMH_Seu, idents = 8)
VMH09Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH09) & VMH_UMAP$UMAP_1 < -19 & VMH_UMAP$UMAP_2 < 18)
VMH10 = subset(VMH_Seu, idents = 9)
VMH10Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH10) & VMH_UMAP$UMAP_1 > -2 & VMH_UMAP$UMAP_1 < 15 & VMH_UMAP$UMAP_2 > -25)
VMH11 = subset(VMH_Seu, idents = 10)
VMH11Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH11) & VMH_UMAP$UMAP_1 > -21 & VMH_UMAP$UMAP_1 < -5 & VMH_UMAP$UMAP_2 > -6 & VMH_UMAP$UMAP_2 < 8)
VMH12 = subset(VMH_Seu, idents = 11)
VMH12Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH12) & VMH_UMAP$UMAP_1 < -18)
VMH13 = subset(VMH_Seu, idents = 12)
VMH13Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH13) & VMH_UMAP$UMAP_2 < -5)
VMH14 = subset(VMH_Seu, idents = 13)
VMH14Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH14) & VMH_UMAP$UMAP_2 < -5)
VMH15 = subset(VMH_Seu, idents = 14)
VMH15Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH15) & VMH_UMAP$UMAP_1 > -10& VMH_UMAP$UMAP_1 < 0)
#CheckInput = VMH04Clean
#CheckUMAP(VMH_Seu)
VMH_REST = subset(VMH_Seu, cells = c(row.names(VMH01Clean), row.names(VMH02Clean), row.names(VMH03Clean), row.names(VMH04Clean), row.names(VMH05Clean),row.names(VMH06Clean),row.names(VMH07Clean), row.names(VMH08Clean), row.names(VMH09Clean), row.names(VMH10Clean), row.names(VMH11Clean), row.names(VMH12Clean), row.names(VMH13Clean), row.names(VMH14Clean),row.names(VMH15Clean)), invert=T)
#CheckInput = VMH06_Extras
#CheckUMAP(VMH_Seu)
##
VMH01_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > -8 & VMH_UMAP$UMAP_2 < 14 & VMH_UMAP$UMAP_2 > -7 | row.names(VMH_UMAP) %in% row.names(VMH01Clean))
VMH03_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -1 & VMH_UMAP$UMAP_2 < -25 | row.names(VMH_UMAP) %in% row.names(VMH03Clean))
VMH05_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -2.5 & VMH_UMAP$UMAP_1 < 5 & VMH_UMAP$UMAP_2 > -10 & VMH_UMAP$UMAP_2 < 0 | row.names(VMH_UMAP) %in% row.names(VMH05Clean))
VMH06_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 4 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > -2 & VMH_UMAP$UMAP_2 < 23 & ! row.names(VMH_UMAP) %in% row.names(VMH05_Extras) | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -1 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > 11 & VMH_UMAP$UMAP_2 < 23 | row.names(VMH_UMAP) %in% row.names(VMH06Clean))
VMH10_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -2 & VMH_UMAP$UMAP_1 < 16 & VMH_UMAP$UMAP_2 > -26 & VMH_UMAP$UMAP_2 < -10 | row.names(VMH_UMAP) %in% row.names(VMH10Clean))
VMH08_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -20 & VMH_UMAP$UMAP_1 < -8 & VMH_UMAP$UMAP_2 > 15 & VMH_UMAP$UMAP_2 < 21 | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -18 & VMH_UMAP$UMAP_1 < -8 & VMH_UMAP$UMAP_2 > 8 | row.names(VMH_UMAP) %in% row.names(VMH08Clean))
VMH13_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 30 & VMH_UMAP$UMAP_2 < -10 | row.names(VMH_UMAP) %in% row.names(VMH13Clean))
VMH14_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 16& VMH_UMAP$UMAP_1 < 30 & VMH_UMAP$UMAP_2 < -5 | row.names(VMH_UMAP) %in% row.names(VMH14Clean))
VMH15_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -8 & VMH_UMAP$UMAP_1 < -1 & VMH_UMAP$UMAP_2 < -25 | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -5 & VMH_UMAP$UMAP_1 < -2 & VMH_UMAP$UMAP_2 < -21 | row.names(VMH_UMAP) %in% row.names(VMH15Clean))
VMH16_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -7 & VMH_UMAP$UMAP_1 < -2 & VMH_UMAP$UMAP_2 > -22 & VMH_UMAP$UMAP_2 < -11 | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -9 & VMH_UMAP$UMAP_1 < -7 & VMH_UMAP$UMAP_2 > -15 & VMH_UMAP$UMAP_2 < -11)
VMH02_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 2 & VMH_UMAP$UMAP_2 > 17 & ! row.names(VMH_UMAP) %in% row.names(VMH06_Extras) | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > 14 | row.names(VMH_UMAP) %in% row.names(VMH02Clean))
VMH07_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -8 & VMH_UMAP$UMAP_2 > -27 & VMH_UMAP$UMAP_2 < -10 & ! row.names(VMH_UMAP) %in% row.names(VMH16_Extras) | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -7 & VMH_UMAP$UMAP_2 > -25 & VMH_UMAP$UMAP_2 < -21 & ! row.names(VMH_UMAP) %in% row.names(VMH16_Extras) | row.names(VMH_UMAP) %in% row.names(VMH07Clean))
VMH09_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -18 & VMH_UMAP$UMAP_2 > 3 & VMH_UMAP$UMAP_2 < 17 & ! row.names(VMH_UMAP) %in% row.names(VMH08_Extras) | row.names(VMH_UMAP) %in% row.names(VMH09Clean))
VMH11_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -20 & VMH_UMAP$UMAP_1 < -10.7 & VMH_UMAP$UMAP_2 > -3.5 & VMH_UMAP$UMAP_2 < 7 & ! row.names(VMH_UMAP) %in% row.names(VMH09_Extras) | row.names(VMH_UMAP) %in% row.names(VMH11Clean))
VMH04_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -1 & VMH_UMAP$UMAP_2 > -12 & VMH_UMAP$UMAP_2 < 0 & ! row.names(VMH_UMAP) %in% c(row.names(VMH07_Extras), row.names(VMH16_Extras), row.names(VMH11_Extras), row.names(VMH05_Extras)) | row.names(VMH_UMAP) %in% row.names(VMH04Clean))
VMH12_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -18 & VMH_UMAP$UMAP_2 > -10 & ! row.names(VMH_UMAP) %in% c(row.names(VMH09_Extras), row.names(VMH08_Extras), row.names(VMH10_Extras)) | row.names(VMH_UMAP) %in% row.names(VMH12Clean))
CheckInput = VMH_Rest2
CheckUMAP(VMH_Seu)
VMH_Assigns_V1 = GenerateMetaData_Barcodes(list("VMH_01" = VMH01_Extras, "VMH_02" = VMH02_Extras,"VMH_03" = VMH03_Extras, "VMH_04" = VMH04_Extras, "VMH_05" = VMH05_Extras, "VMH_06" = VMH06_Extras, "VMH_07" = VMH07_Extras, "VMH_08" = VMH08_Extras, "VMH_09" = VMH09_Extras, "VMH_10" = VMH10_Extras, "VMH_11" = VMH11_Extras, "VMH_12" = VMH12_Extras,"VMH_13" = VMH13_Extras, "VMH_14" = VMH14_Extras, "VMH_15" = VMH15_Extras, "VMH_16" = VMH16_Extras))
VMH_Rest2 = subset(VMH_Seu, cells = VMH_Assigns_V1$Barcodes, invert=T)
VMH_Assigns_V1$Dups = duplicated(VMH_Assigns_V1$Barcodes) | duplicated(VMH_Assigns_V1$Barcodes, fromLast=T)
VMH_Assigns_T = subset(VMH_Assigns_V1, VMH_Assigns_V1$Dups == T)
unique(VMH_Assigns_T$Pop)
VMH_Assigns = GenerateMetaData(list("VMH_01" = VMH01_Extras, "VMH_02" = VMH02_Extras,"VMH_03" = VMH03_Extras, "VMH_04" = VMH04_Extras, "VMH_05" = VMH05_Extras, "VMH_06" = VMH06_Extras, "VMH_07" = VMH07_Extras, "VMH_08" = VMH08_Extras, "VMH_09" = VMH09_Extras, "VMH_10" = VMH10_Extras, "VMH_11" = VMH11_Extras, "VMH_12" = VMH12_Extras,"VMH_13" = VMH13_Extras, "VMH_14" = VMH14_Extras, "VMH_15" = VMH15_Extras, "VMH_16" = VMH16_Extras, "VMH_05" = VMH_Rest2))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
SMN
GetSMNBarcs = subset(MainAssign, MainAssign$Nuclei == "SMN")
SMN_Seu = subset(EdKaZhouHypoNeurons, cells = GetSMNBarcs$Barcs)
DefaultAssay(SMN_Seu) = "integrated"
SMN_Seu = FindVariableFeatures(SMN_Seu)
SMN_Seu = ScaleData(SMN_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(SMN_Seu) = "integrated"
SMN_Seu = RunPCA(SMN_Seu, npcs = 30)
SMN_Seu <- RunUMAP(SMN_Seu, dims = 1:30, spread= 2)
DefaultAssay(SMN_Seu) = "RNA"
set.dim = 30
set.res = 1
set.kparam = c(50)
##ClusterFunc_All_RNA(SMN_Seu)
DefaultAssay(SMN_Seu) = "integrated"
SMN_Seu <- FindNeighbors(SMN_Seu, k.param=50, dims=1:30)
SMN_Seu <- FindClusters(SMN_Seu, resolution = 1)
DefaultAssay(SMN_Seu) = "RNA"
SMN_UMAP = as.data.frame(SMN_Seu@reductions$umap@cell.embeddings)
SMN01 = subset(SMN_Seu, idents = c(0, 1, 11, 16))
SMN01Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN01) & SMN_UMAP$UMAP_1 > -9 & SMN_UMAP$UMAP_1 < 1.5 & SMN_UMAP$UMAP_2 > -9 & SMN_UMAP$UMAP_2 < 1 | row.names(SMN_UMAP) %in% colnames(SMN01) & SMN_UMAP$UMAP_1 > -10 & SMN_UMAP$UMAP_1 < -5 & SMN_UMAP$UMAP_2 < -5)
SMN02 = subset(SMN_Seu, idents = c(2, 9))
SMN03 = subset(SMN_Seu, idents = 3)
SMN03Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN03) & SMN_UMAP$UMAP_2 > -8)
SMN04 = subset(SMN_Seu, idents = 4)
SMN04Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN04) & SMN_UMAP$UMAP_1 > -2 & SMN_UMAP$UMAP_1 < 5)
SMN05 = subset(SMN_Seu, idents = c(5, 8, 13, 15,18))
SMN05Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN05) & SMN_UMAP$UMAP_2 < 10)
SMN06 = subset(SMN_Seu, idents = 6)
SMN07 = subset(SMN_Seu, idents = 7)
SMN07Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN07) & SMN_UMAP$UMAP_1 > 5)
SMN08 = subset(SMN_Seu, idents = 10)
SMN08Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN08) & SMN_UMAP$UMAP_1 > 5 & SMN_UMAP$UMAP_2 < -5)
SMN09 = subset(SMN_Seu, idents = c(12,14))
SMN09Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN09) & SMN_UMAP$UMAP_1 > -4)
SMN10 = subset(SMN_Seu, idents = 17)
SMN10Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN10) & SMN_UMAP$UMAP_2 > 6 & SMN_UMAP$UMAP_2 < 11 & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 10)
#CheckInput = SMN_REST
#CheckUMAP(SMN_Seu)
SMN_REST = subset(SMN_Seu, cells = c(colnames(SMN02), colnames(SMN06), row.names(SMN01Clean), row.names(SMN03Clean), row.names(SMN04Clean), row.names(SMN05Clean),row.names(SMN07Clean), row.names(SMN08Clean), row.names(SMN09Clean),row.names(SMN10Clean)), invert=T)
#CheckInput = SMN10_Extras
#CheckUMAP(SMN_Seu)
SMN01_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -10 & SMN_UMAP$UMAP_1 < 1.5 & SMN_UMAP$UMAP_2 > -9 & SMN_UMAP$UMAP_2 < 1 | row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -10 & SMN_UMAP$UMAP_1 < -5 & SMN_UMAP$UMAP_2 < -5 | row.names(SMN_UMAP) %in% row.names(SMN01Clean))
SMN02_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 0 & SMN_UMAP$UMAP_1 < 10 & SMN_UMAP$UMAP_2 > 11 | row.names(SMN_UMAP) %in% colnames(SMN02))
SMN03_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 < -10 & SMN_UMAP$UMAP_2 < 0 & ! row.names(SMN_UMAP) %in% row.names(SMN01_Extras)| row.names(SMN_UMAP) %in% row.names(SMN03Clean))
SMN04_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 0 & SMN_UMAP$UMAP_1 < 5 & SMN_UMAP$UMAP_2 < -5| row.names(SMN_UMAP) %in% row.names(SMN04Clean))
SMN05_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -2 & SMN_UMAP$UMAP_2 > -5 & SMN_UMAP$UMAP_2 < 7 & ! row.names(SMN_UMAP) %in% row.names(SMN01_Extras) | row.names(SMN_UMAP) %in% row.names(SMN05Clean))
SMN06_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 < -10 & SMN_UMAP$UMAP_2 > 10 | row.names(SMN_UMAP) %in% colnames(SMN06))
SMN07_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 10 & SMN_UMAP$UMAP_2 > 5 & ! row.names(SMN_UMAP) %in% row.names(SMN02_Extras) | row.names(SMN_UMAP) %in% row.names(SMN07Clean))
SMN08_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 5 & SMN_UMAP$UMAP_2 < -5 | row.names(SMN_UMAP) %in% row.names(SMN08Clean))
SMN09_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -6 & SMN_UMAP$UMAP_1 < 4 & SMN_UMAP$UMAP_2 < -4 & ! row.names(SMN_UMAP) %in% c(row.names(SMN01_Extras), row.names(SMN04_Extras)) | row.names(SMN_UMAP) %in% row.names(SMN09Clean))
SMN10_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_2 > 6 & SMN_UMAP$UMAP_2 < 11 & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 10 | row.names(SMN_UMAP) %in% row.names(SMN10Clean))
SMN_Assigns = GenerateMetaData_Barcodes(list("SMN_01" = SMN01_Extras, "SMN_02" = SMN02_Extras, "SMN_03" = SMN03_Extras, "SMN_04" = SMN04_Extras, "SMN_05" = SMN05_Extras, "SMN_06" = SMN06_Extras, "SMN_07" = SMN07_Extras, "SMN_08" = SMN08_Extras, "SMN_09" = SMN09_Extras, "SMN_10" = SMN10_Extras))
#SMN_REST2 = subset(SMN_Seu, cells = SMN_Assigns$Barcodes, invert=T)
#CheckInput = SMN_REST2
#CheckUMAP(SMN_Seu)
SMN01_Recluster = subset(SMN_Seu, cells = row.names(SMN01_Extras))
set.dim = 30
set.res = 1
set.kparam = c(50, 80)
##ClusterFunc_All_RNA(SMN01_Recluster)
DefaultAssay(SMN01_Recluster) = "integrated"
SMN01_Recluster <- FindNeighbors(SMN01_Recluster, k.param=40, dims=1:30)
SMN01_Recluster <- FindClusters(SMN01_Recluster, resolution = 1)
DefaultAssay(SMN01_Recluster) = "RNA"
SMN11 = subset(SMN01_Recluster, idents = c(1,9, 5))
SMN12 = subset(SMN01_Recluster, idents = c(4))
SMN13 = subset(SMN01_Recluster, idents = c(2))
SMN11Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN11) & SMN_UMAP$UMAP_2 > -10 & SMN_UMAP$UMAP_2 < -5 & SMN_UMAP$UMAP_1 < -1)
SMN12Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN12) & SMN_UMAP$UMAP_2 < -10)
SMN13Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN13) & SMN_UMAP$UMAP_1 < -7 & SMN_UMAP$UMAP_2 > -11)
SMN_REST3 = subset(SMN01_Recluster, cells = c(row.names(SMN11Clean), row.names(SMN12Clean), row.names(SMN13Clean)), invert=T)
SMN11_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_2 > -10 & SMN_UMAP$UMAP_2 < -6 & SMN_UMAP$UMAP_1 < -1 & SMN_UMAP$UMAP_1 > -7.5 | row.names(SMN_UMAP) %in% row.names(SMN11Clean))
SMN12_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_2 < -10 | row.names(SMN_UMAP) %in% row.names(SMN12Clean))
SMN13_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_1 < -8 & ! row.names(SMN_UMAP) %in% c(row.names(SMN11_Extras), row.names(SMN12_Extras)) | row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_1 < -6 & SMN_UMAP$UMAP_2 < -6 & ! row.names(SMN_UMAP) %in% c(row.names(SMN11_Extras), row.names(SMN12_Extras)) | row.names(SMN_UMAP) %in% row.names(SMN13Clean))
SMN01_New = subset(SMN01_Recluster, cells = c(row.names(SMN11_Extras), row.names(SMN12_Extras), row.names(SMN13_Extras)), invert=T)
SMN05_Seu = subset(SMN_Seu, cells = row.names(SMN05_Extras))
set.dim = 30
set.res = 1
set.kparam = c(40)
#ClusterFunc_All_RNA(SMN05_Seu)
DefaultAssay(SMN05_Seu) = "integrated"
SMN05_Seu <- FindNeighbors(SMN05_Seu, k.param=40, dims=1:30)
SMN05_Seu <- FindClusters(SMN05_Seu, resolution = 1)
SMN_14 = subset(SMN05_Seu, idents = c(0,1))
SMN_15 = subset(SMN05_Seu, idents = c(2,9))
SMN_16 = subset(SMN05_Seu, idents = c(3))
SMN_17 = subset(SMN05_Seu, idents = c(4))
SMN_18 = subset(SMN05_Seu, idents = c(5))
SMN_19 = subset(SMN05_Seu, idents = c(6))
SMN_20 = subset(SMN05_Seu, idents = c(7))
SMN_05_New = subset(SMN05_Seu, idents = c(8))
SMN14Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_14) & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 7 & SMN_UMAP$UMAP_2 > 0)
SMN_15
SMN16Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_16) & SMN_UMAP$UMAP_1 > 6 & SMN_UMAP$UMAP_2 > -2 & SMN_UMAP$UMAP_2 < 2)
SMN17Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_17) & SMN_UMAP$UMAP_1 > 6.5 & SMN_UMAP$UMAP_1 < 11 & SMN_UMAP$UMAP_2 > 2)
SMN18Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_18) & SMN_UMAP$UMAP_1 < 4.5 & SMN_UMAP$UMAP_2 < 4.5)
SMN_19
SMN20Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_20) & SMN_UMAP$UMAP_1 > 8)
SMN_05_New
SMN_REST4 = subset(SMN05_Seu, cells = c(row.names(SMN14Clean), row.names(SMN16Clean), row.names(SMN17Clean), row.names(SMN18Clean), row.names(SMN20Clean), colnames(SMN_15), colnames(SMN_19), colnames(SMN_05_New)), invert=T)
SMN14Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 6.5 & SMN_UMAP$UMAP_2 > 1.6 | row.names(SMN_UMAP) %in% row.names(SMN14Clean) )
SMN15Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 8 & SMN_UMAP$UMAP_2 < 0 & SMN_UMAP$UMAP_2 > -2 | row.names(SMN_UMAP) %in% colnames(SMN_15) )
SMN16Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 6.5 & SMN_UMAP$UMAP_2 < 2.3 & SMN_UMAP$UMAP_2 > -1 & ! row.names(SMN_UMAP) %in% row.names(SMN15Extras) | row.names(SMN_UMAP) %in% row.names(SMN16Clean) )
SMN17Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 6.5 & SMN_UMAP$UMAP_1 < 11 & SMN_UMAP$UMAP_2 > 2 | row.names(SMN_UMAP) %in% row.names(SMN17Clean) )
SMN18Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 3.7 & SMN_UMAP$UMAP_2 < 3 & SMN_UMAP$UMAP_2 > 0 & ! row.names(SMN_UMAP) %in% row.names(SMN14Extras) | row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 5 & SMN_UMAP$UMAP_2 < 2 & SMN_UMAP$UMAP_2 > 0 & ! row.names(SMN_UMAP) %in% row.names(SMN14Extras) | row.names(SMN_UMAP) %in% row.names(SMN18Clean) )
SMN19Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 4 & ! row.names(SMN_UMAP) %in% c(row.names(SMN14Extras), row.names(SMN18Extras)) | row.names(SMN_UMAP) %in% colnames(SMN_19) )
SMN20Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 8 & SMN_UMAP$UMAP_2 > 2.5 & ! row.names(SMN_UMAP) %in% row.names(SMN17Extras) | row.names(SMN_UMAP) %in% row.names(SMN20Clean) )
SMN05_New_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 10 & SMN_UMAP$UMAP_2 < 1 & SMN_UMAP$UMAP_1 > 4 & ! row.names(SMN_UMAP) %in% c(row.names(SMN15Extras), row.names(SMN16Extras)) | row.names(SMN_UMAP) %in% colnames(SMN_05_New) )
SMN_Assigns = GenerateMetaData_Barcodes(list("SMN_01" = SMN01_New, "SMN_02" = SMN02_Extras, "SMN_03" = SMN03_Extras, "SMN_04" = SMN04_Extras, "SMN_05" = SMN05_New_Extras, "SMN_06" = SMN06_Extras, "SMN_07" = SMN07_Extras, "SMN_08" = SMN08_Extras, "SMN_09" = SMN09_Extras, "SMN_10" = SMN10_Extras, "SMN_11" = SMN11_Extras, "SMN_12" = SMN12_Extras, "SMN_13" = SMN13_Extras, "SMN_14" = SMN14Extras, "SMN_15" = SMN15Extras, "SMN_16" = SMN16Extras, "SMN_17" = SMN17Extras, "SMN_18" = SMN18Extras, "SMN_19" = SMN19Extras, "SMN_20" = SMN20Extras))
#CheckInput = SMN05_New_Extras
#CheckUMAP(SMN05_Seu)
SMN_Assigns$Dups = duplicated(SMN_Assigns$Barcodes) | duplicated(SMN_Assigns$Barcodes, fromLast=T)
SMN_Assigns_T = subset(SMN_Assigns, SMN_Assigns$Dups == T)
unique(SMN_Assigns_T$Pop)
SMN_Assigns = GenerateMetaData(list("SMN_01" = SMN01_New, "SMN_02" = SMN02_Extras, "SMN_03" = SMN03_Extras, "SMN_04" = SMN04_Extras, "SMN_05" = SMN05_New_Extras, "SMN_06" = SMN06_Extras, "SMN_07" = SMN07_Extras, "SMN_08" = SMN08_Extras, "SMN_09" = SMN09_Extras, "SMN_10" = SMN10_Extras, "SMN_11" = SMN11_Extras, "SMN_12" = SMN12_Extras, "SMN_13" = SMN13_Extras, "SMN_14" = SMN14Extras, "SMN_15" = SMN15Extras, "SMN_16" = SMN16Extras, "SMN_17" = SMN17Extras, "SMN_18" = SMN18Extras, "SMN_19" = SMN19Extras, "SMN_20" = SMN20Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
LH
GetLHBarcs = subset(MainAssign, MainAssign$Nuclei == "LH")
LH_Seu = subset(EdKaZhouHypoNeurons, cells = GetLHBarcs$Barcs)
DefaultAssay(LH_Seu) = "integrated"
LH_Seu = FindVariableFeatures(LH_Seu)
LH_Seu = ScaleData(LH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(LH_Seu) = "integrated"
LH_Seu = RunPCA(LH_Seu, npcs = 20)
LH_Seu <- RunHarmony(LH_Seu, group.by.vars = "SampleAdult")
LH_Seu <- RunUMAP(LH_Seu, dims = 1:20, spread= 5, reduction = "harmony")
DefaultAssay(LH_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(5)
#ClusterFunc_All_RNA(LH_Seu)
DefaultAssay(LH_Seu) = "integrated"
LH_Seu <- FindNeighbors(LH_Seu, k.param=5, dims=1:20)
LH_Seu <- FindClusters(LH_Seu, resolution = 1)
DefaultAssay(LH_Seu) = "RNA"
#CheckInput = LH08
#CheckUMAP(LH_Seu)
LH_UMAP = as.data.frame(LH_Seu@reductions$umap@cell.embeddings)
LH01 = subset(LH_Seu, idents = c(0,3,4))
LH01Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH01) & LH_UMAP$UMAP_1 < -3 & LH_UMAP$UMAP_2 > -9.5 & LH_UMAP$UMAP_2 < 10)
LH02 = subset(LH_Seu, idents = 1)
LH02Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH02) & LH_UMAP$UMAP_1 < -3 & LH_UMAP$UMAP_2 > 10 & LH_UMAP$UMAP_2 < 20)
LH03 = subset(LH_Seu, idents = c(2))
LH03Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH03) & LH_UMAP$UMAP_1 > -3 & LH_UMAP$UMAP_1 < 8 & LH_UMAP$UMAP_2 > -8 & LH_UMAP$UMAP_2 < 5)
LH04 = subset(LH_Seu, idents = 5)
LH04Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH04) & LH_UMAP$UMAP_1 > -5 & LH_UMAP$UMAP_1 < 10)
LH05 = subset(LH_Seu, idents = c(6,8))
LH05Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH05) & LH_UMAP$UMAP_1 > 8 & LH_UMAP$UMAP_1 < 21 & LH_UMAP$UMAP_2 > -20 & LH_UMAP$UMAP_2 < -7)
LH06 = subset(LH_Seu, idents = 7)
LH06Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH06) & LH_UMAP$UMAP_1 > 10 & LH_UMAP$UMAP_2 > 0)
LH07 = subset(LH_Seu, idents = 10)
LH08 = subset(LH_Seu, idents = 11)
#CheckInput = LH10Clean
#CheckUMAP(LH_Seu)
LH_REST = subset(LH_Seu, cells = c(row.names(LH01Clean), row.names(LH02Clean), row.names(LH03Clean), row.names(LH04Clean), row.names(LH05Clean),row.names(LH06Clean), colnames(LH07), colnames(LH08)), invert=T)
#CheckInput = LH08_Extras
#CheckUMAP(LH_Seu)
LH01_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 < -4 & LH_UMAP$UMAP_2 < 9 & LH_UMAP$UMAP_2 > -7 | row.names(LH_UMAP) %in% row.names(LH01Clean))
LH02_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 < -4 & LH_UMAP$UMAP_2 > 9 | row.names(LH_UMAP) %in% row.names(LH02Clean))
LH03_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > -1 & LH_UMAP$UMAP_1 < 9 & LH_UMAP$UMAP_2 < 3 & LH_UMAP$UMAP_2 > -9 | row.names(LH_UMAP) %in% row.names(LH03Clean))
LH04_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > -6 & LH_UMAP$UMAP_1 < 8 & LH_UMAP$UMAP_2 > 15 | row.names(LH_UMAP) %in% row.names(LH04Clean))
LH05_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 8 & LH_UMAP$UMAP_1 < 20 & LH_UMAP$UMAP_2 < -9 & LH_UMAP$UMAP_2 > -16 | row.names(LH_UMAP) %in% row.names(LH05Clean))
LH06_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 20 & LH_UMAP$UMAP_2 > 0 | row.names(LH_UMAP) %in% row.names(LH06Clean))
LH08_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 8 & LH_UMAP$UMAP_1 < 13 & LH_UMAP$UMAP_2 > -8 & LH_UMAP$UMAP_2 < -2 | row.names(LH_UMAP) %in% colnames(LH08))
LH09_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 10 & LH_UMAP$UMAP_1 < 20 & LH_UMAP$UMAP_2 > 0 & LH_UMAP$UMAP_2 < 10)
LH10_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 < -7 & LH_UMAP$UMAP_2 < -10)
LH_Assigns = GenerateMetaData_Barcodes(list( "LH_01" = LH01_Extras, "LH_02" = LH02_Extras, "LH_03" = LH03_Extras, "LH_04" = LH04_Extras, "LH_05" = LH05_Extras, "LH_06" = LH06_Extras, "LH_07" = LH07, "LH_08" = LH08_Extras, "LH_09" = LH09_Extras, "LH_10" = LH10_Extras))
LH_REST2 = subset(LH_Seu, cells = LH_Assigns$Barcodes, invert=T)
#CheckInput = LH10_Extras
#CheckUMAP(LH_Seu)
LH_Assigns$Dups = duplicated(LH_Assigns$Barcodes) | duplicated(LH_Assigns$Barcodes, fromLast=T)
LH_Assigns_T = subset(LH_Assigns, LH_Assigns$Dups == T)
unique(LH_Assigns_T$Pop)
LH_Assigns = GenerateMetaData(list( "LH_01" = LH01_Extras, "LH_02" = LH02_Extras, "LH_03" = LH03_Extras, "LH_04" = LH04_Extras, "LH_05" = LH05_Extras, "LH_06" = LH06_Extras, "LH_07" = LH07, "LH_08" = LH08_Extras, "LH_09" = LH09_Extras, "LH_10" = LH10_Extras, "LH_11" = LH_REST2))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
SCN
GetSCNBarcs = subset(MainAssign, MainAssign$Nuclei == "SCN")
SCN_Seu = subset(EdKaZhouHypoNeurons, cells = GetSCNBarcs$Barcs)
DefaultAssay(SCN_Seu) = "integrated"
SCN_Seu = FindVariableFeatures(SCN_Seu)
SCN_Seu = ScaleData(SCN_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(SCN_Seu) = "integrated"
SCN_Seu = RunPCA(SCN_Seu, npcs = 20)
SCN_Seu <- RunUMAP(SCN_Seu, dims = 1:20, spread= 2)
DefaultAssay(SCN_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(50)
##ClusterFunc_All_RNA(SCN_Seu)
DefaultAssay(SCN_Seu) = "integrated"
SCN_Seu <- FindNeighbors(SCN_Seu, k.param=50, dims=1:20)
SCN_Seu <- FindClusters(SCN_Seu, resolution = 1)
DefaultAssay(SCN_Seu) = "RNA"
#CheckInput = SCN08Clean
#CheckUMAP(SCN_Seu)
SCN_UMAP = as.data.frame(SCN_Seu@reductions$umap@cell.embeddings)
SCN01 = subset(SCN_Seu, idents = c(0))
SCN01Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN01) & SCN_UMAP$UMAP_1 > -3 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 > -8 & SCN_UMAP$UMAP_2 < 1)
SCN02 = subset(SCN_Seu, idents = c(1,6,13,22))
SCN02Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN02) & SCN_UMAP$UMAP_1 < -6 & SCN_UMAP$UMAP_2 < 7)
SCN03 = subset(SCN_Seu, idents = c(2,20))
SCN03Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN03) & SCN_UMAP$UMAP_1 > 4 & SCN_UMAP$UMAP_1 < 12 & SCN_UMAP$UMAP_2 > 5)
SCN04 = subset(SCN_Seu, idents = c(3,18))
SCN04Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN04) & SCN_UMAP$UMAP_1 < -3 & SCN_UMAP$UMAP_1 > -11 & SCN_UMAP$UMAP_2 > 0)
SCN05 = subset(SCN_Seu, idents = 4)
SCN05Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN05) & SCN_UMAP$UMAP_1 > 6 & SCN_UMAP$UMAP_2 < 0)
SCN06 = subset(SCN_Seu, idents = 5)
SCN06Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN06) & SCN_UMAP$UMAP_1 < 3 & SCN_UMAP$UMAP_2 > -14 & SCN_UMAP$UMAP_2 < -8.5)
SCN07 = subset(SCN_Seu, idents = c(7,15))
SCN07Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN07) & SCN_UMAP$UMAP_1 > -4 & SCN_UMAP$UMAP_1 < 7 & SCN_UMAP$UMAP_2 > -3 & SCN_UMAP$UMAP_2 < 5)
SCN08 = subset(SCN_Seu, idents = c(8))
SCN08Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN08) & SCN_UMAP$UMAP_1 > 5 & SCN_UMAP$UMAP_1 < 11 & SCN_UMAP$UMAP_2 > -2 & SCN_UMAP$UMAP_2 < 8)
SCN09 = subset(SCN_Seu, idents = c(9))
SCN09Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN09) & SCN_UMAP$UMAP_1 > 0 & SCN_UMAP$UMAP_2 < -5 & SCN_UMAP$UMAP_2 > -10)
SCN10 = subset(SCN_Seu, idents = c(10))
SCN10Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN10) & SCN_UMAP$UMAP_1 > -5 & SCN_UMAP$UMAP_1 < 0)
SCN11 = subset(SCN_Seu, idents = c(11))
SCN11Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN11) & SCN_UMAP$UMAP_1 > 1 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 > -7 & SCN_UMAP$UMAP_2 < 0)
SCN12 = subset(SCN_Seu, idents = c(12))
SCN12Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN12) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_2 < 4)
SCN13 = subset(SCN_Seu, idents = c(14))
SCN13Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN13) & SCN_UMAP$UMAP_1 < 4 )
SCN14 = subset(SCN_Seu, idents = c(16))
SCN14Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN14) & SCN_UMAP$UMAP_2 > 4.5 )
SCN15 = subset(SCN_Seu, idents = c(17))
SCN15Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN15) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_2 > 0)
SCN16 = subset(SCN_Seu, idents = c(19))
SCN17 = subset(SCN_Seu, idents = c(21))
#CheckInput = SCN14Clean
#CheckUMAP(SCN_Seu)
SCN_REST = subset(SCN_Seu, cells = c(row.names(SCN01Clean), row.names(SCN02Clean), row.names(SCN03Clean), row.names(SCN04Clean), row.names(SCN05Clean), row.names(SCN06Clean), row.names(SCN07Clean), row.names(SCN08Clean), row.names(SCN09Clean), row.names(SCN10Clean), row.names(SCN11Clean), row.names(SCN12Clean), row.names(SCN13Clean), row.names(SCN14Clean), row.names(SCN15Clean), colnames(SCN16), colnames(SCN17)), invert=T)
#CheckInput = SCN01_Extras
#CheckUMAP(SCN_Seu)
SCN02_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < -10 & SCN_UMAP$UMAP_2 < 8 | row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < -6 & SCN_UMAP$UMAP_2 < 4 | row.names(SCN_UMAP) %in% row.names(SCN02Clean))
SCN03_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 3.5 & SCN_UMAP$UMAP_1 < 12 & SCN_UMAP$UMAP_2 > 7.4 | row.names(SCN_UMAP) %in% row.names(SCN03Clean))
SCN04_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < -2 & SCN_UMAP$UMAP_2 > 2 & ! row.names(SCN_UMAP) %in% row.names(SCN02_Extras) | row.names(SCN_UMAP) %in% row.names(SCN04Clean))
SCN05_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 7 & SCN_UMAP$UMAP_2 > -10 & SCN_UMAP$UMAP_2 < -1 | row.names(SCN_UMAP) %in% row.names(SCN05Clean))
SCN08_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 5 & SCN_UMAP$UMAP_1 < 9 & SCN_UMAP$UMAP_2 > -2 & SCN_UMAP$UMAP_2 < 4 & ! row.names(SCN_UMAP) %in% c( row.names(SCN05_Extras))| row.names(SCN_UMAP) %in% row.names(SCN08Clean))
SCN09_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 0 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 < -6.2 & SCN_UMAP$UMAP_2 > -10 | row.names(SCN_UMAP) %in% row.names(SCN09Clean))
SCN06_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < 3 & SCN_UMAP$UMAP_2 > -14 & SCN_UMAP$UMAP_2 < -8.5 & ! row.names(SCN_UMAP) %in% row.names(SCN09_Extras) | row.names(SCN_UMAP) %in% row.names(SCN06Clean))
SCN11_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 4 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 > -5 & SCN_UMAP$UMAP_2 < 0 & ! row.names(SCN_UMAP) %in% c(row.names(SCN08_Extras), row.names(SCN05_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN11Clean))
SCN01_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > -2 & SCN_UMAP$UMAP_1 < 7 & SCN_UMAP$UMAP_2 > -8 & SCN_UMAP$UMAP_2 < -2 & ! row.names(SCN_UMAP) %in% c(row.names(SCN05_Extras), row.names(SCN11_Extras), row.names(SCN08_Extras), row.names(SCN09_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN01Clean))
SCN10_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > -5 & SCN_UMAP$UMAP_1 < 0 & SCN_UMAP$UMAP_2 < 0 & ! row.names(SCN_UMAP) %in% c(row.names(SCN01_Extras), row.names(SCN06_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN10Clean))
SCN07_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > -4 & SCN_UMAP$UMAP_1 < 7 & SCN_UMAP$UMAP_2 > -5 & SCN_UMAP$UMAP_2 < 5 & ! row.names(SCN_UMAP) %in% c(row.names(SCN01_Extras), row.names(SCN08_Extras), row.names(SCN11_Extras), row.names(SCN10_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN07Clean))
SCN12_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_1 < 14.5 & SCN_UMAP$UMAP_2 < 4.5 & SCN_UMAP$UMAP_2 > -1 | row.names(SCN_UMAP) %in% row.names(SCN12Clean))
SCN13_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 0 & SCN_UMAP$UMAP_1 < 6 & SCN_UMAP$UMAP_2 > 8 & ! row.names(SCN_UMAP) %in% row.names(SCN03_Extras) | row.names(SCN_UMAP) %in% row.names(SCN13Clean))
SCN14_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_1 < 13 & SCN_UMAP$UMAP_2 > 4.5 & SCN_UMAP$UMAP_2 < 9 & ! row.names(SCN_UMAP) %in% row.names(SCN03_Extras) | row.names(SCN_UMAP) %in% row.names(SCN14Clean))
SCN15_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_2 > 3 & SCN_UMAP$UMAP_2 < 9 & ! row.names(SCN_UMAP) %in% c(row.names(SCN14_Extras), row.names(SCN12_Extras), row.names(SCN03_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN15Clean))
SCN16_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_2 < -12 & SCN_UMAP$UMAP_1 < 5 & ! row.names(SCN_UMAP) %in% row.names(SCN06_Extras) | row.names(SCN_UMAP) %in% colnames(SCN16))
SCN17_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 5 & SCN_UMAP$UMAP_2 < -11 | row.names(SCN_UMAP) %in% colnames(SCN17))
SCN_Assigns = GenerateMetaData_Barcodes(list( "SCN_01" = SCN01_Extras, "SCN_02" = SCN02_Extras, "SCN_03" = SCN03_Extras, "SCN_04" = SCN04_Extras, "SCN_05" = SCN05_Extras, "SCN_06" = SCN06_Extras, "SCN_07" = SCN07_Extras, "SCN_08" = SCN08_Extras, "SCN_09" = SCN09_Extras, "SCN_10" = SCN10_Extras, "SCN_11" = SCN11_Extras, "SCN_12" = SCN12_Extras, "SCN_13" = SCN13_Extras, "SCN_14" = SCN14_Extras, "SCN_15" = SCN15_Extras, "SCN_16" = SCN16_Extras, "SCN_17" = SCN17_Extras))
SCN_REST2 = subset(SCN_Seu, cells = SCN_Assigns$Barcodes, invert=T)
SCN_Assigns$Dups = duplicated(SCN_Assigns$Barcodes) | duplicated(SCN_Assigns$Barcodes, fromLast=T)
SCN_Assigns_T = subset(SCN_Assigns, SCN_Assigns$Dups == T)
unique(SCN_Assigns_T$Pop)
SCN_Assigns = GenerateMetaData(list( "SCN_01" = SCN01_Extras, "SCN_02" = SCN02_Extras, "SCN_03" = SCN03_Extras, "SCN_04" = SCN04_Extras, "SCN_05" = SCN05_Extras, "SCN_06" = SCN06_Extras, "SCN_07" = SCN07_Extras, "SCN_08" = SCN08_Extras, "SCN_09" = SCN09_Extras, "SCN_10" = SCN10_Extras, "SCN_11" = SCN11_Extras, "SCN_12" = SCN12_Extras, "SCN_13" = SCN13_Extras, "SCN_14" = SCN14_Extras, "SCN_15" = SCN15_Extras, "SCN_16" = SCN16_Extras, "SCN_17" = SCN17_Extras, "SCN_18" = SCN_REST2))
CheckInput = SCN03_Extras
CheckUMAP(SCN_Seu)
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
DMH
GetDMHBarcs = subset(MainAssign, MainAssign$Nuclei == "DMH")
DMH_Seu = subset(EdKaZhouHypoNeurons, cells = GetDMHBarcs$Barcs)
DefaultAssay(DMH_Seu) = "integrated"
DMH_Seu = FindVariableFeatures(DMH_Seu)
DMH_Seu = ScaleData(DMH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(DMH_Seu) = "integrated"
DMH_Seu = RunPCA(DMH_Seu, npcs = 20)
DMH_Seu <- RunUMAP(DMH_Seu, dims = 1:20, spread= 5)
DefaultAssay(DMH_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(20,50)
#ClusterFunc_All_RNA(DMH_Seu)
#CheckInput = DMH07Clean
#CheckUMAP(DMH_Seu)
DefaultAssay(DMH_Seu) = "integrated"
DMH_Seu <- FindNeighbors(DMH_Seu, k.param=20, dims=1:20)
DMH_Seu <- FindClusters(DMH_Seu, resolution = 1)
DefaultAssay(DMH_Seu) = "RNA"
DMH_UMAP = as.data.frame(DMH_Seu@reductions$umap@cell.embeddings)
DMH01 = subset(DMH_Seu, idents = 0)
DMH02 = subset(DMH_Seu, idents = 1)
DMH02Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH02) & DMH_UMAP$UMAP_1 > 7 & DMH_UMAP$UMAP_2 < -7 & DMH_UMAP$UMAP_1 < 22)
DMH03 = subset(DMH_Seu, idents = 2)
DMH03Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH03) & DMH_UMAP$UMAP_1 > -16 & DMH_UMAP$UMAP_1 < -6 & DMH_UMAP$UMAP_2 > 7 )
DMH04 = subset(DMH_Seu, idents = 3)
DMH04Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH04) & DMH_UMAP$UMAP_2 > 0)
DMH05 = subset(DMH_Seu, idents = 4)
DMH05Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH05) & DMH_UMAP$UMAP_2 > -7)
DMH06 = subset(DMH_Seu, idents = 5)
DMH06Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH06) & DMH_UMAP$UMAP_2 > -23 & DMH_UMAP$UMAP_2 < -11)
DMH07 = subset(DMH_Seu, idents = 6)
DMH07Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH07) & DMH_UMAP$UMAP_2 > -10 & DMH_UMAP$UMAP_2 < 11)
DMH08 = subset(DMH_Seu, idents = 7)
DMH08Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH08) & DMH_UMAP$UMAP_2 < 11)
DMH09 = subset(DMH_Seu, idents = 8)
DMH10 = subset(DMH_Seu, idents = 9)
DMH10Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH10) & DMH_UMAP$UMAP_1 < -20)
DMH11 = subset(DMH_Seu, idents = 10)
DMH11Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH11) & DMH_UMAP$UMAP_2 < -20)
DMH12 = subset(DMH_Seu, idents = 11)
#CheckInput = DMH04Clean
#CheckUMAP(DMH_Seu)
DMH_REST = subset(DMH_Seu, cells = c(colnames(DMH01), row.names(DMH02Clean), row.names(DMH03Clean), row.names(DMH04Clean), row.names(DMH05Clean),row.names(DMH06Clean), row.names(DMH07Clean), row.names(DMH08Clean), colnames(DMH09), row.names(DMH10Clean), row.names(DMH11Clean), colnames(DMH12)), invert=T)
#CheckInput = DMH07_Extras
#CheckUMAP(DMH_Seu)
DMH02_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > 5 & DMH_UMAP$UMAP_1 < 18 & DMH_UMAP$UMAP_2 < 0 | row.names(DMH_UMAP) %in% row.names(DMH02Clean))
DMH03_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST)& DMH_UMAP$UMAP_1 > -16 & DMH_UMAP$UMAP_1 < -6 & DMH_UMAP$UMAP_2 > 7 | row.names(DMH_UMAP) %in% row.names(DMH03Clean))
DMH04_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_2 > 3 & DMH_UMAP$UMAP_2 < 15 & DMH_UMAP$UMAP_1 < -10 & ! row.names(DMH_UMAP) %in% row.names(DMH03_Extras) | row.names(DMH_UMAP) %in% row.names(DMH04Clean))
DMH05_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_2 > -10 & DMH_UMAP$UMAP_1 > 20 | row.names(DMH_UMAP) %in% row.names(DMH05Clean))
DMH06_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 5 & DMH_UMAP$UMAP_2 > -22 & DMH_UMAP$UMAP_2 < -12 | row.names(DMH_UMAP) %in% row.names(DMH06Clean))
DMH07_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 10 & DMH_UMAP$UMAP_1 > -8 & DMH_UMAP$UMAP_2 > -1 & DMH_UMAP$UMAP_2 < 9 | row.names(DMH_UMAP) %in% row.names(DMH07Clean))
DMH01_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > -8 & DMH_UMAP$UMAP_2 > 7 & ! row.names(DMH_UMAP) %in% row.names(DMH07_Extras) | row.names(DMH_UMAP) %in% colnames(DMH01))
DMH08_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 1 & DMH_UMAP$UMAP_2 > -9 & DMH_UMAP$UMAP_2 < -1 & ! row.names(DMH_UMAP) %in% row.names(DMH07_Extras) | row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > -9 & DMH_UMAP$UMAP_1 < -7 & DMH_UMAP$UMAP_2 > 0 & DMH_UMAP$UMAP_2 < 2 | row.names(DMH_UMAP) %in% row.names(DMH08Clean))
DMH09_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > 10 & DMH_UMAP$UMAP_2 < -5 & ! row.names(DMH_UMAP) %in% row.names(DMH02_Extras) | row.names(DMH_UMAP) %in% colnames(DMH09))
DMH11_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 10 & DMH_UMAP$UMAP_2 < -22 | row.names(DMH_UMAP) %in% row.names(DMH11Clean))
DMH12_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > -18 & DMH_UMAP$UMAP_1 < -10 & DMH_UMAP$UMAP_2 > -2 & DMH_UMAP$UMAP_2 < 5 & ! row.names(DMH_UMAP) %in% row.names(DMH04_Extras) | row.names(DMH_UMAP) %in% colnames(DMH12))
DMH_Assigns = GenerateMetaData_Barcodes(list("DMH_01" = DMH01_Extras, "DMH_02" = DMH02_Extras,"DMH_03" = DMH03_Extras, "DMH_04" = DMH04_Extras, "DMH_05" = DMH05_Extras, "DMH_06" = DMH06_Extras, "DMH_07" = DMH07_Extras, "DMH_08" = DMH08_Extras, "DMH_09" = DMH09_Extras, "DMH_10" = DMH10Clean, "DMH_11" = DMH11_Extras, "DMH_12" = DMH12_Extras))
DMH13 = subset(DMH_Seu, cells = DMH_Assigns$Barcodes, invert=T)
DMH_Assigns$Dups = duplicated(DMH_Assigns$Barcodes) | duplicated(DMH_Assigns$Barcodes, fromLast=T)
DMH_Assigns_T = subset(DMH_Assigns, DMH_Assigns$Dups == T)
unique(DMH_Assigns_T$Pop)
DMH_Assigns = GenerateMetaData(list("DMH_01" = DMH01_Extras, "DMH_02" = DMH02_Extras,"DMH_03" = DMH03_Extras, "DMH_04" = DMH04_Extras, "DMH_05" = DMH05_Extras, "DMH_06" = DMH06_Extras, "DMH_07" = DMH07_Extras, "DMH_08" = DMH08_Extras, "DMH_09" = DMH09_Extras, "DMH_10" = DMH10Clean, "DMH_11" = DMH11_Extras, "DMH_12" = DMH12_Extras, "DMH_13" = DMH13))
#CheckInput = DMH_Assigns
#CheckUMAP(DMH_Seu)
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
PO
GetPOBarcs = subset(MainAssign, MainAssign$Nuclei == "PO")
PO_Seu = subset(EdKaZhouHypoNeurons, cells = GetPOBarcs$Barcs)
DefaultAssay(PO_Seu) = "integrated"
PO_Seu = FindVariableFeatures(PO_Seu)
PO_Seu = ScaleData(PO_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(PO_Seu) = "integrated"
PO_Seu = RunPCA(PO_Seu, npcs = 20)
PO_Seu <- RunUMAP(PO_Seu, dims = 1:20, spread= 5)
DefaultAssay(PO_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(30, 40)
#ClusterFunc_All_RNA(PO_Seu)
#CheckInput = PO10Clean
#CheckUMAP(PO_Seu)
DefaultAssay(PO_Seu) = "integrated"
PO_Seu <- FindNeighbors(PO_Seu, k.param=20, dims=1:20)
PO_Seu <- FindClusters(PO_Seu, resolution = 1)
DefaultAssay(PO_Seu) = "RNA"
PO_UMAP = as.data.frame(PO_Seu@reductions$umap@cell.embeddings)
PO01 = subset(PO_Seu, idents = c(0, 12))
PO01Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO01) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 < -6 )
PO02 = subset(PO_Seu, idents = c(1,8))
PO02Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO02) & PO_UMAP$UMAP_1 > 2 & PO_UMAP$UMAP_2 < 16 & PO_UMAP$UMAP_2 > 5 | row.names(PO_UMAP) %in% colnames(PO02) & PO_UMAP$UMAP_1 > 6 & PO_UMAP$UMAP_2 < 5 & PO_UMAP$UMAP_2 > -4 )
PO03 = subset(PO_Seu, idents = 2)
PO03Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO03) & PO_UMAP$UMAP_2 > 5 )
PO04 = subset(PO_Seu, idents = c(3,4))
PO04Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO04) &PO_UMAP$UMAP_2 < -5.5 & PO_UMAP$UMAP_1 > 0)
PO05 = subset(PO_Seu, idents = 5)
PO05Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO05) & PO_UMAP$UMAP_2 > 10 & PO_UMAP$UMAP_1 > 0 & PO_UMAP$UMAP_1 < 12)
PO06 = subset(PO_Seu, idents = 6)
PO06Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO06) & PO_UMAP$UMAP_1 < 1& PO_UMAP$UMAP_2 > -5 & PO_UMAP$UMAP_2 < 10)
PO07 = subset(PO_Seu, idents = 7)
PO07Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO07) & PO_UMAP$UMAP_1 > -10 & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 > -10 & PO_UMAP$UMAP_2 < 5)
PO08 = subset(PO_Seu, idents = 9)
PO09 = subset(PO_Seu, idents = 10)
PO09Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO09) & PO_UMAP$UMAP_1 < 11)
PO10 = subset(PO_Seu, idents = 11)
PO10Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO10) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_1 > -10)
#CheckInput = PO06Clean
#CheckUMAP(PO_Seu)
PO_REST = subset(PO_Seu, cells = c(row.names(PO01Clean), row.names(PO02Clean), row.names(PO03Clean), row.names(PO04Clean), row.names(PO05Clean),row.names(PO06Clean), row.names(PO07Clean), colnames(PO08), row.names(PO09Clean), row.names(PO10Clean)), invert=T)
#CheckInput = PO10_Extras
#CheckUMAP(PO_Seu)
PO01_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 < -6 | row.names(PO_UMAP) %in% row.names(PO01Clean))
PO02_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 2 & PO_UMAP$UMAP_2 < 13 & PO_UMAP$UMAP_2 > 3.5 | row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 6 & PO_UMAP$UMAP_1 > 6 & PO_UMAP$UMAP_2 < 5 & PO_UMAP$UMAP_2 > -3 | row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 12 & PO_UMAP$UMAP_2 < 19 & PO_UMAP$UMAP_2 > 5 | row.names(PO_UMAP) %in% row.names(PO02Clean))
PO03_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST)& PO_UMAP$UMAP_1 < -10 & PO_UMAP$UMAP_2 > 6 | row.names(PO_UMAP) %in% row.names(PO03Clean))
PO05_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_2 > 13 & PO_UMAP$UMAP_1 > 0 & PO_UMAP$UMAP_1 < 12 | row.names(PO_UMAP) %in% row.names(PO05Clean))
PO06_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > -15 & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 > 2 & PO_UMAP$UMAP_2 < 7 | row.names(PO_UMAP) %in% row.names(PO06Clean))
PO07_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > -10 & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 > -10 & PO_UMAP$UMAP_2 < 5 & ! row.names(PO_UMAP) %in% row.names(PO06_Extras)| row.names(PO_UMAP) %in% row.names(PO07Clean))
PO08_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 < -10 & PO_UMAP$UMAP_2 > -8 & PO_UMAP$UMAP_2 < 4 & ! row.names(PO_UMAP) %in% row.names(PO06_Extras) | row.names(PO_UMAP) %in% colnames(PO08))
PO09_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 0 & PO_UMAP$UMAP_1 < 10 & PO_UMAP$UMAP_2 > -12 & PO_UMAP$UMAP_2 < 3 & ! row.names(PO_UMAP) %in% row.names(PO02_Extras) | row.names(PO_UMAP) %in% row.names(PO09Clean))
PO04_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_2 < 0 & PO_UMAP$UMAP_1 > 0 & ! row.names(PO_UMAP) %in% c(row.names(PO02_Extras), row.names(PO09_Extras)) | row.names(PO_UMAP) %in% row.names(PO04Clean))
PO10_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_1 > -10 & PO_UMAP$UMAP_2 > 8 & ! row.names(PO_UMAP) %in% c(row.names(PO03_Extras), row.names(PO06_Extras)) | row.names(PO_UMAP) %in% row.names(PO10Clean))
PO_Assigns = GenerateMetaData_Barcodes(list("PO_01" = PO01_Extras, "PO_02" = PO02_Extras,"PO_03" = PO03_Extras, "PO_04" = PO04_Extras, "PO_05" = PO05_Extras, "PO_06" = PO06_Extras, "PO_07" = PO07_Extras, "PO_08" = PO08_Extras, "PO_09" = PO09_Extras, "PO_10" = PO10_Extras))
PO_6pt2 = subset(PO_Seu, cells = PO_Assigns$Barcodes, invert=T)
PO_Assigns$Dups = duplicated(PO_Assigns$Barcodes) | duplicated(PO_Assigns$Barcodes, fromLast=T)
PO_Assigns_T = subset(PO_Assigns, PO_Assigns$Dups == T)
unique(PO_Assigns_T$Pop)
PO_Assigns = GenerateMetaData(list("PO_01" = PO01_Extras, "PO_02" = PO02_Extras,"PO_03" = PO03_Extras, "PO_04" = PO04_Extras, "PO_05" = PO05_Extras,"PO_06" = PO06_Extras, "PO_06" = PO_6pt2, "PO_07" = PO07_Extras, "PO_08" = PO08_Extras, "PO_09" = PO09_Extras, "PO_10" = PO10_Extras))
#CheckInput = PO_REST2
#CheckUMAP(PO_Seu)
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
AH
GetAHBarcs = subset(MainAssign, MainAssign$Nuclei == "AH")
AH_Seu = subset(EdKaZhouHypoNeurons, cells = GetAHBarcs$Barcs)
DefaultAssay(AH_Seu) = "integrated"
AH_Seu = FindVariableFeatures(AH_Seu)
AH_Seu = ScaleData(AH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(AH_Seu) = "integrated"
AH_Seu = RunPCA(AH_Seu, npcs = 20)
AH_Seu <- RunHarmony(AH_Seu, group.by.vars = "SampleAdult")
AH_Seu <- RunUMAP(AH_Seu, dims = 1:20, spread= 5, reduction = "harmony")
DefaultAssay(AH_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(10,100)
##ClusterFunc_All_RNA(AH_Seu)
#CheckInput = AH01Clean
#CheckUMAP(AH_Seu)
DefaultAssay(AH_Seu) = "integrated"
AH_Seu <- FindNeighbors(AH_Seu, k.param=20, dims=1:20)
AH_Seu <- FindClusters(AH_Seu, resolution = 1)
DefaultAssay(AH_Seu) = "RNA"
AH_UMAP = as.data.frame(AH_Seu@reductions$umap@cell.embeddings)
AH01 = subset(AH_Seu, idents = c(3))
AH01Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH01) & AH_UMAP$UMAP_1 > 10 & AH_UMAP$UMAP_1 < 26 & AH_UMAP$UMAP_2 > -7 )
AH02 = subset(AH_Seu, idents = c(4))
AH02Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH02) & AH_UMAP$UMAP_1 > 0.5 & AH_UMAP$UMAP_2 < -10.5)
AH03 = subset(AH_Seu, idents = c(5,18))
AH03Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH03) & AH_UMAP$UMAP_1 > -18 & AH_UMAP$UMAP_2 > 5)
AH04 = subset(AH_Seu, idents = c(6))
AH04Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH04) & AH_UMAP$UMAP_2 > 23)
AH05 = subset(AH_Seu, idents = 7)
AH05Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH05) & AH_UMAP$UMAP_2 < -23 )
AH06 = subset(AH_Seu, idents = c(8,16,9))
AH06Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH06) & AH_UMAP$UMAP_1 > 20)
AH07Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH06) & AH_UMAP$UMAP_1 < 20 & AH_UMAP$UMAP_2 < 5)
AH08 = subset(AH_Seu, idents = c(10,19))
AH08Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH08) & AH_UMAP$UMAP_1 < -10 & AH_UMAP$UMAP_2 < -10)
AH09 = subset(AH_Seu, idents = c(11))
AH09Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH09) & AH_UMAP$UMAP_2 < 24)
AH10 = subset(AH_Seu, idents = c(12))
AH11 = subset(AH_Seu, idents = c(13,20))
AH11Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH11) & AH_UMAP$UMAP_1 < -10)
AH12 = subset(AH_Seu, idents = c(14))
AH12Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH12) & AH_UMAP$UMAP_2 < -10)
AH13 = subset(AH_Seu, idents = c(15,17))
AH14 = subset(AH_Seu, idents = c(21))
AH14Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH14) & AH_UMAP$UMAP_1 > 0 & AH_UMAP$UMAP_1 < 15)
AH15 = subset(AH_Seu, idents = c(22))
#CheckInput = AH12Clean
#CheckUMAP(AH_Seu)
AH_REST = subset(AH_Seu, cells = c(row.names(AH01Clean), row.names(AH02Clean), row.names(AH03Clean), row.names(AH04Clean), row.names(AH05Clean),row.names(AH06Clean), row.names(AH07Clean), row.names(AH08Clean), row.names(AH09Clean), row.names(AH11Clean), row.names(AH12Clean), colnames(AH10), colnames(AH13),row.names(AH14Clean), colnames(AH15)), invert=T)
#CheckInput = AH08Clean
#CheckUMAP(AH_Seu)
AH01_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < 25 & AH_UMAP$UMAP_1 > 11 & AH_UMAP$UMAP_2 > -7.2 & AH_UMAP$UMAP_2 < 20 | row.names(AH_UMAP) %in% row.names(AH01Clean))
AH02_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 2 & AH_UMAP$UMAP_1 < 10 & AH_UMAP$UMAP_2 > -16 & AH_UMAP$UMAP_2 < -10 | row.names(AH_UMAP) %in% row.names(AH02Clean))
AH03_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -19 & AH_UMAP$UMAP_1 < -10 & AH_UMAP$UMAP_2 > 7 & AH_UMAP$UMAP_2 < 18 | row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -12 & AH_UMAP$UMAP_1 < 0 & AH_UMAP$UMAP_2 > 15 | row.names(AH_UMAP) %in% row.names(AH03Clean))
AH04_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 0 & AH_UMAP$UMAP_2 > 23 | row.names(AH_UMAP) %in% row.names(AH04Clean))
AH05_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_2 < -21 & AH_UMAP$UMAP_1 > -10 & AH_UMAP$UMAP_1 < 8 | row.names(AH_UMAP) %in% row.names(AH05Clean))
AH06_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 23 & AH_UMAP$UMAP_2 > -10 & AH_UMAP$UMAP_2 < 10 & ! row.names(AH_UMAP) %in% row.names(AH01_Extras)| row.names(AH_UMAP) %in% row.names(AH06Clean))
AH07_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 0 & AH_UMAP$UMAP_1 < 11 & AH_UMAP$UMAP_2 > -3 & AH_UMAP$UMAP_2 < 5 | row.names(AH_UMAP) %in% row.names(AH07Clean))
AH08_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < -18 & AH_UMAP$UMAP_2 < -5 | row.names(AH_UMAP) %in% row.names(AH08Clean))
AH09_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -2 & AH_UMAP$UMAP_1 < 12 & AH_UMAP$UMAP_2 > 5 & ! row.names(AH_UMAP) %in% row.names(AH04_Extras) | row.names(AH_UMAP) %in% row.names(AH09Clean))
AH10_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 12 & AH_UMAP$UMAP_1 < 25 & AH_UMAP$UMAP_2 > -18 & AH_UMAP$UMAP_2 < -7 & ! row.names(AH_UMAP) %in% row.names(AH01_Extras) | row.names(AH_UMAP) %in% colnames(AH10))
AH11_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < -22 & AH_UMAP$UMAP_2 > 13 | row.names(AH_UMAP) %in% row.names(AH11Clean))
AH12_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 9 & AH_UMAP$UMAP_2 < -20 | row.names(AH_UMAP) %in% row.names(AH12Clean))
AH13_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < -25 & AH_UMAP$UMAP_2 > -5 & AH_UMAP$UMAP_2 < 15 | row.names(AH_UMAP) %in% colnames(AH13))
AH14_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 3 & AH_UMAP$UMAP_1 < 13 & AH_UMAP$UMAP_2 > -22 & AH_UMAP$UMAP_2 < -15 | row.names(AH_UMAP) %in% row.names(AH14Clean))
AH15_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -15 & AH_UMAP$UMAP_1 < -8 & AH_UMAP$UMAP_2 > -10 & AH_UMAP$UMAP_2 < -3 | row.names(AH_UMAP) %in% colnames(AH15))
#CheckInput = AH17_Extras
#CheckUMAP(AH_Seu)
AH_REST2 = subset(AH_Seu, cells = c(row.names(AH01_Extras), row.names(AH02_Extras), row.names(AH03_Extras), row.names(AH04_Extras), row.names(AH05_Extras),row.names(AH06_Extras), row.names(AH07_Extras), row.names(AH08_Extras), row.names(AH09_Extras), row.names(AH11_Extras), row.names(AH12_Extras), row.names(AH10_Extras), row.names(AH13_Extras), row.names(AH14_Extras), row.names(AH15_Extras)), invert=T)
AH16_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST2) & AH_UMAP$UMAP_2 < 10)
AH17_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST2) & AH_UMAP$UMAP_2 > 10)
AH_Assigns = GenerateMetaData_Barcodes(list("AH_01" = AH01_Extras, "AH_02" = AH02_Extras,"AH_03" = AH03_Extras, "AH_04" = AH04_Extras, "AH_05" = AH05_Extras, "AH_06" = AH06_Extras, "AH_07" = AH07_Extras, "AH_08" = AH08_Extras, "AH_09" = AH09_Extras, "AH_10" = AH10_Extras, "AH_11" = AH11_Extras, "AH_12" = AH12_Extras,"AH_13" = AH13_Extras, "AH_14" = AH14_Extras, "AH_15" = AH15_Extras))
#AH_01_Rest = subset(AH_Seu, cells = AH_Assigns$Barcodes, invert=T)
AH_Assigns$Dups = duplicated(AH_Assigns$Barcodes) | duplicated(AH_Assigns$Barcodes, fromLast=T)
AH_Assigns_T = subset(AH_Assigns, AH_Assigns$Dups == T)
unique(AH_Assigns_T$Pop)
AH_Assigns = GenerateMetaData(list("AH_01" = AH01_Extras, "AH_02" = AH02_Extras,"AH_03" = AH03_Extras, "AH_04" = AH04_Extras, "AH_05" = AH05_Extras, "AH_06" = AH06_Extras, "AH_07" = AH07_Extras, "AH_08" = AH08_Extras, "AH_09" = AH09_Extras, "AH_10" = AH10_Extras, "AH_11" = AH11_Extras, "AH_12" = AH12_Extras,"AH_13" = AH13_Extras, "AH_14" = AH14_Extras, "AH_15" = AH15_Extras, "AH_16" = AH16_Extras, "AH_17" = AH17_Extras))
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns", "AH_Seu", "AH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
MN
GetMNBarcs = subset(MainAssign, MainAssign$Nuclei == "MN")
MN_Seu = subset(EdKaZhouHypoNeurons, cells = GetMNBarcs$Barcs)
DefaultAssay(MN_Seu) = "integrated"
MN_Seu = FindVariableFeatures(MN_Seu)
MN_Seu = ScaleData(MN_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(MN_Seu) = "integrated"
MN_Seu = RunPCA(MN_Seu, npcs = 20)
MN_Seu <- RunHarmony(MN_Seu, group.by.vars = "SampleAdult")
MN_Seu <- RunUMAP(MN_Seu, dims = 1:20, spread= 5, reduction="harmony")
DefaultAssay(MN_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(50, 100)
#ClusterFunc_All_RNA(MN_Seu)
DefaultAssay(MN_Seu) = "integrated"
MN_Seu <- FindNeighbors(MN_Seu, k.param=50, dims=1:20)
MN_Seu <- FindClusters(MN_Seu, resolution = 1)
DefaultAssay(MN_Seu) = "RNA"
MN_UMAP = as.data.frame(MN_Seu@reductions$umap@cell.embeddings)
MN01 = subset(MN_Seu, idents = c(0))
MN01Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN01) & MN_UMAP$UMAP_1 < -17 & MN_UMAP$UMAP_2 > -17 & MN_UMAP$UMAP_2 < 11)
MN02 = subset(MN_Seu, idents = c(1,3))
MN02Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN02) & MN_UMAP$UMAP_1 > -14 & MN_UMAP$UMAP_1 < 0 & MN_UMAP$UMAP_2 > 4)
MN22Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN02) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < 10 & MN_UMAP$UMAP_2 > 0)
MN23Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN02) & MN_UMAP$UMAP_1 > -3 & MN_UMAP$UMAP_1 < 6 & MN_UMAP$UMAP_2 > -5 & MN_UMAP$UMAP_2 < 4)
#CheckInput = MN06Clean
#CheckUMAP(MN_Seu)
MN03 = subset(MN_Seu, idents = 2)
MN03Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN03) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 10.5 & MN_UMAP$UMAP_2 > -3 & MN_UMAP$UMAP_2 < 10)
MN22_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN03) & MN_UMAP$UMAP_1 > 10.5 & MN_UMAP$UMAP_1 < 17 & MN_UMAP$UMAP_2 > -3 & MN_UMAP$UMAP_2 < 10)
MN04 = subset(MN_Seu, idents = c(4))
MN04Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN04) & MN_UMAP$UMAP_2 > 10)
MN05 = subset(MN_Seu, idents = 6)
MN05Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN05) & MN_UMAP$UMAP_1 > -16 & MN_UMAP$UMAP_1 < 0 & MN_UMAP$UMAP_2 < -15)
MN24Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN05) & MN_UMAP$UMAP_2 > -15 & MN_UMAP$UMAP_2 < -9 & MN_UMAP$UMAP_1 < -12)
MN06 = subset(MN_Seu, idents = c(7, 9))
MN06Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN06) & MN_UMAP$UMAP_1 > 22 & MN_UMAP$UMAP_2 > -14 | row.names(MN_UMAP) %in% colnames(MN06) & MN_UMAP$UMAP_1 > 21 & MN_UMAP$UMAP_2 < -1 & MN_UMAP$UMAP_2 > -15 | row.names(MN_UMAP) %in% colnames(MN06) & MN_UMAP$UMAP_1 > 17 & MN_UMAP$UMAP_2 < -9 & MN_UMAP$UMAP_2 > -14)
MN07 = subset(MN_Seu, idents = 8)
MN07Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN07) & MN_UMAP$UMAP_1 > -12 & MN_UMAP$UMAP_1 < -4 & MN_UMAP$UMAP_2 > -9 & MN_UMAP$UMAP_2 < 1 )
MN25Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN07) & MN_UMAP$UMAP_1 > -18 & MN_UMAP$UMAP_1 < -12 & MN_UMAP$UMAP_2 > -11 & MN_UMAP$UMAP_2 < 3)
MN08 = subset(MN_Seu, idents = 10)
MN08Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN08) & MN_UMAP$UMAP_1 > -3 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 > 10)
MN26Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN08) & MN_UMAP$UMAP_1 > 14 & MN_UMAP$UMAP_2 > -4 & MN_UMAP$UMAP_2 <10)
MN09 = subset(MN_Seu, idents = c(11))
MN09Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN09) & MN_UMAP$UMAP_1 > 7 & MN_UMAP$UMAP_1 < 14)
MN10Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN09) & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_1 > 1 & MN_UMAP$UMAP_2 > -15 & MN_UMAP$UMAP_2 < -10)
MN11Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN09) & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -3)
MN12 = subset(MN_Seu, idents = c(12))
MN12Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN12) & MN_UMAP$UMAP_1 > -17 & MN_UMAP$UMAP_1 < -7 & MN_UMAP$UMAP_2 > -8)
MN13 = subset(MN_Seu, idents = c(13))
MN13Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN13) & MN_UMAP$UMAP_1 > -5 & MN_UMAP$UMAP_1 < 3 &MN_UMAP$UMAP_2 < -15)
MN14Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN13) & MN_UMAP$UMAP_1 > 11 & MN_UMAP$UMAP_1 < 17 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < 0)
MN15 = subset(MN_Seu, idents = c(14))
MN15Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN15) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < -13)
MN16 = subset(MN_Seu, idents = c(15))
MN16Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN16) & MN_UMAP$UMAP_1 > -22 & MN_UMAP$UMAP_1 < -10 & MN_UMAP$UMAP_2 < 8)
MN17 = subset(MN_Seu, idents = c(16))
MN17Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN17) & MN_UMAP$UMAP_1 > 11)
MN18 = subset(MN_Seu, idents = c(17))
MN18Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN18) & MN_UMAP$UMAP_1 > -11 & MN_UMAP$UMAP_1 < 1 & MN_UMAP$UMAP_2 > -18 & MN_UMAP$UMAP_2 < -9)
MN19 = subset(MN_Seu, idents = c(18))
MN19Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN19) & MN_UMAP$UMAP_1 > 1 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -1)
MN20 = subset(MN_Seu, idents = c(19))
MN20Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN20) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_1 < 22)
MN21 = subset(MN_Seu, idents = c(20))
MN21Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN21) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 < -15)
MN_REST = subset(MN_Seu, cells = c(row.names(MN01Clean), row.names(MN02Clean), row.names(MN03Clean), row.names(MN04Clean), row.names(MN05Clean), row.names(MN06Clean), row.names(MN07Clean), row.names(MN08Clean), row.names(MN09Clean), row.names(MN10Clean), row.names(MN11Clean), row.names(MN12Clean), row.names(MN13Clean), row.names(MN14Clean), row.names(MN15Clean), row.names(MN16Clean), row.names(MN17Clean), row.names(MN18Clean), row.names(MN19Clean), row.names(MN20Clean), row.names(MN21Clean), row.names(MN22_Pt2), row.names(MN22Clean), row.names(MN23Clean), row.names(MN24Clean), row.names(MN25Clean), row.names(MN26Clean)), invert=T)
MN02_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST)& MN_UMAP$UMAP_1 > -12 & MN_UMAP$UMAP_1 < -3 & MN_UMAP$UMAP_2 > 3 | row.names(MN_UMAP) %in% row.names(MN02Clean))
MN03_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 2 & MN_UMAP$UMAP_1 < 9 & MN_UMAP$UMAP_2 > -1 & MN_UMAP$UMAP_2 < 12 | row.names(MN_UMAP) %in% row.names(MN03Clean))
MN04_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 > 10 | row.names(MN_UMAP) %in% row.names(MN04Clean))
MN06_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 21.5 & MN_UMAP$UMAP_2 > -12 & MN_UMAP$UMAP_2 < 5 | row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 20.6 & MN_UMAP$UMAP_2 > -12 & MN_UMAP$UMAP_2 < 0 | row.names(MN_UMAP) %in% row.names(MN06Clean))
MN07_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -12 & MN_UMAP$UMAP_1 < -5 & MN_UMAP$UMAP_2 > -9 & MN_UMAP$UMAP_2 < -1 | row.names(MN_UMAP) %in% row.names(MN07Clean))
MN08_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -8 & MN_UMAP$UMAP_1 < 8 & MN_UMAP$UMAP_2 > 12 & ! row.names(MN_UMAP) %in% row.names(MN02_Extras) | row.names(MN_UMAP) %in% row.names(MN08Clean))
MN09_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 7 & MN_UMAP$UMAP_1 < 13 & MN_UMAP$UMAP_2 > -13 & MN_UMAP$UMAP_2 < -5 | row.names(MN_UMAP) %in% row.names(MN09Clean))
MN10_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_2 > -16 & MN_UMAP$UMAP_2 < -9 | row.names(MN_UMAP) %in% row.names(MN10Clean))
MN11_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -1 & MN_UMAP$UMAP_1 < 8 & MN_UMAP$UMAP_2 > -9 & MN_UMAP$UMAP_2 < -5 & ! row.names(MN_UMAP) %in% row.names(MN09_Extras)| row.names(MN_UMAP) %in% c(row.names(MN11Clean), row.names(MN19Clean)))
MN13_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -4 & MN_UMAP$UMAP_1 < 1 & MN_UMAP$UMAP_2 > -21 & MN_UMAP$UMAP_2 < -16 | row.names(MN_UMAP) %in% row.names(MN13Clean))
MN05_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -17 & MN_UMAP$UMAP_1 < -3 & MN_UMAP$UMAP_2 < -16 & ! row.names(MN_UMAP) %in% row.names(MN13_Extras)| row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -3 & MN_UMAP$UMAP_1 < 1 & MN_UMAP$UMAP_2 < -23 & ! row.names(MN_UMAP) %in% row.names(MN13_Extras)| row.names(MN_UMAP) %in% row.names(MN05Clean))
MN14_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_1 < 17 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < 0 & ! row.names(MN_UMAP) %in% row.names(MN09_Extras)| MN_UMAP$UMAP_1 > 15 & MN_UMAP$UMAP_1 < 18 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -8 | row.names(MN_UMAP) %in% row.names(MN14Clean))
MN15_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 14 & MN_UMAP$UMAP_2 < -21 | row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 20 & MN_UMAP$UMAP_2 < -12| row.names(MN_UMAP) %in% row.names(MN15Clean))
MN16_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -20 & MN_UMAP$UMAP_1 < -13 & MN_UMAP$UMAP_2 > -5 & MN_UMAP$UMAP_2 < 5 | row.names(MN_UMAP) %in% row.names(MN16Clean))
MN01_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 < -20 | row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 < -18 & MN_UMAP$UMAP_2 < -3 & MN_UMAP$UMAP_2 > -10 & ! row.names(MN_UMAP) %in% row.names(MN16_Extras) | row.names(MN_UMAP) %in% row.names(MN01Clean))
MN12_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -15 & MN_UMAP$UMAP_1 < -7 & MN_UMAP$UMAP_2 > -6 & ! row.names(MN_UMAP) %in% c(row.names(MN02_Extras), row.names(MN07_Extras), row.names(MN16_Extras)) | row.names(MN_UMAP) %in% row.names(MN12Clean))
MN17_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < -12 & ! row.names(MN_UMAP) %in% c(row.names(MN15_Extras), row.names(MN06_Extras))| row.names(MN_UMAP) %in% row.names(MN17Clean))
MN18_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 2 & MN_UMAP$UMAP_2 > -18 & MN_UMAP$UMAP_2 < -9 & ! row.names(MN_UMAP) %in% c(row.names(MN05_Extras), row.names(MN13_Extras))| row.names(MN_UMAP) %in% row.names(MN18Clean))
MN10_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_2 > -16 & MN_UMAP$UMAP_2 < -9 & ! row.names(MN_UMAP) %in% row.names(MN18_Extras) | row.names(MN_UMAP) %in% row.names(MN10Clean))
MN19_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 16 & MN_UMAP$UMAP_1 < 24 & MN_UMAP$UMAP_2 > -1 & MN_UMAP$UMAP_2 < 6 & ! row.names(MN_UMAP) %in% row.names(MN05_Extras)| row.names(MN_UMAP) %in% row.names(MN26Clean))
MN20_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 15 & MN_UMAP$UMAP_1 < 22 & MN_UMAP$UMAP_2 > -11 & MN_UMAP$UMAP_2 < 0 & ! row.names(MN_UMAP) %in% c(row.names(MN06_Extras), row.names(MN14_Extras), row.names(MN19_Extras))| row.names(MN_UMAP) %in% row.names(MN20Clean))
MN21_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 < -18 & ! row.names(MN_UMAP) %in% c(row.names(MN05_Extras), row.names(MN13_Extras))| row.names(MN_UMAP) %in% row.names(MN21Clean))
MN22_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 8 & MN_UMAP$UMAP_1 < 22 & MN_UMAP$UMAP_2 > -1 & MN_UMAP$UMAP_2 < 10 & ! row.names(MN_UMAP) %in% c(row.names(MN19_Extras), row.names(MN03_Extras))| row.names(MN_UMAP) %in% c(row.names(MN22Clean), row.names(MN22_Pt2)))
MN23_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -4 & MN_UMAP$UMAP_1 < 5 & MN_UMAP$UMAP_2 > -5 & MN_UMAP$UMAP_2 < 4 & ! row.names(MN_UMAP) %in% c(row.names(MN03_Extras), row.names(MN11_Extras))| row.names(MN_UMAP) %in% row.names(MN23Clean))
MN24_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 < -11 & MN_UMAP$UMAP_2 > -17 & MN_UMAP$UMAP_2 < -10 & ! row.names(MN_UMAP) %in% c(row.names(MN05_Extras), row.names(MN01_Extras))| row.names(MN_UMAP) %in% row.names(MN24Clean))
MN25_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -19 & MN_UMAP$UMAP_1 < -10 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -4 & ! row.names(MN_UMAP) %in% c(row.names(MN05Clean), row.names(MN07_Extras), row.names(MN01_Extras), row.names(MN16_Extras), row.names(MN12_Extras))| row.names(MN_UMAP) %in% row.names(MN25Clean))
#CheckInput = MN_Rest2
#CheckUMAP(MN_Seu)
#############
MN_Assigns_V1 = GenerateMetaData_Barcodes(list("MN_01" = MN01_Extras, "MN_02" = MN02_Extras,"MN_03" = MN03_Extras, "MN_04" = MN04_Extras, "MN_05" = MN05_Extras, "MN_06" = MN06_Extras, "MN_07" = MN07_Extras, "MN_08" = MN08_Extras, "MN_09" = MN09_Extras, "MN_10" = MN10_Extras, "MN_11" = MN11_Extras, "MN_12" = MN12_Extras,"MN_13" = MN13_Extras, "MN_14" = MN14_Extras, "MN_15" = MN15_Extras, "MN_16" = MN16_Extras, "MN_17" = MN17_Extras, "MN_18" = MN18_Extras, "MN_19" = MN19_Extras,"MN_20" = MN20_Extras, "MN_21" = MN21_Extras, "MN_22" = MN22_Extras,"MN_23" = MN23_Extras, "MN_24" = MN24_Extras, "MN_25" = MN25_Extras))
MN_Rest2 = subset(MN_Seu, cells = MN_Assigns_V1$Barcodes, invert=T)
MN01_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 < -10 & MN_UMAP$UMAP_2 > 10 | row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -8 & MN_UMAP$UMAP_1 < -1 & MN_UMAP$UMAP_2 > 3)
MN05_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2)& MN_UMAP$UMAP_1 < 3 & MN_UMAP$UMAP_2 < -20)
MN08_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 5 & MN_UMAP$UMAP_2 > 8 & ! row.names(MN_UMAP) %in% row.names(MN01_Pt2))
MN11_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < 0)
MN12_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 < -12 & MN_UMAP$UMAP_2 > 0 & ! row.names(MN_UMAP) %in% row.names(MN01_Pt2))
MN17_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < -10)
MN23_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 5 & MN_UMAP$UMAP_2 > -2 & MN_UMAP$UMAP_2 < 10 & ! row.names(MN_UMAP) %in% c(row.names(MN01_Pt2), row.names(MN08_Pt2), row.names(MN11_Pt2)))
MN_Assigns = GenerateMetaData(list("MN_01" = MN01_Extras, "MN_02" = MN02_Extras,"MN_03" = MN03_Extras, "MN_04" = MN04_Extras, "MN_05" = MN05_Extras, "MN_06" = MN06_Extras, "MN_07" = MN07_Extras, "MN_08" = MN08_Extras, "MN_09" = MN09_Extras, "MN_10" = MN10_Extras, "MN_11" = MN11_Extras, "MN_12" = MN12_Extras,"MN_13" = MN13_Extras, "MN_14" = MN14_Extras, "MN_15" = MN15_Extras, "MN_16" = MN16_Extras, "MN_17" = MN17_Extras, "MN_18" = MN18_Extras, "MN_19" = MN19_Extras,"MN_20" = MN20_Extras, "MN_21" = MN21_Extras, "MN_22" = MN22_Extras,"MN_23" = MN23_Extras, "MN_24" = MN24_Extras, "MN_25" = MN25_Extras, "MN_01" = MN01_Pt2, "MN_05" = MN05_Pt2, "MN_08" = MN08_Pt2, "MN_11" = MN11_Pt2, "MN_17" = MN17_Pt2, "MN_12" = MN12_Pt2, "MN_23" = MN23_Pt2))
#MN_Assigns$Dups = duplicated(MN_Assigns$Barcodes) | duplicated(MN_Assigns$Barcodes, fromLast=T)
#MN_Assigns_T = subset(MN_Assigns, MN_Assigns$Dups == T)
#unique(MN_Assigns_T$Pop)
#CheckInput = MN12_Pt2
#CheckUMAP(MN_Seu)
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns", "AH_Seu", "AH_Assigns", "MN_Seu", "MN_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
UNASSIGNED
GetUNASSIGNEDBarcs = subset(MainAssign, MainAssign$Nuclei == "NA")
UNASSIGNED_Seu = subset(EdKaZhouHypoNeurons, cells = GetUNASSIGNEDBarcs$Barcs)
DefaultAssay(UNASSIGNED_Seu) = "integrated"
UNASSIGNED_Seu = FindVariableFeatures(UNASSIGNED_Seu)
UNASSIGNED_Seu = ScaleData(UNASSIGNED_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F)
DefaultAssay(UNASSIGNED_Seu) = "integrated"
UNASSIGNED_Seu = RunPCA(UNASSIGNED_Seu, npcs = 20)
UNASSIGNED_Seu <- RunUMAP(UNASSIGNED_Seu, dims = 1:20, spread= 5)
DefaultAssay(UNASSIGNED_Seu) = "RNA"
set.dim = 20
set.res = 1
set.kparam = c(50, 100)
#ClusterFunc_All_RNA(UNASSIGNED_Seu)
#CheckInput = UNASSIGNED01Clean
#CheckUMAP(UNASSIGNED_Seu)
DefaultAssay(UNASSIGNED_Seu) = "integrated"
UNASSIGNED_Seu <- FindNeighbors(UNASSIGNED_Seu, k.param=50, dims=1:20)
UNASSIGNED_Seu <- FindClusters(UNASSIGNED_Seu, resolution = 1)
DefaultAssay(UNASSIGNED_Seu) = "RNA"
UNASSIGNED_UMAP = as.data.frame(UNASSIGNED_Seu@reductions$umap@cell.embeddings)
UNASSIGNED01 = subset(UNASSIGNED_Seu, idents = c(0))
UNASSIGNED01Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED01) & UNASSIGNED_UMAP$UMAP_1 > -5 & UNASSIGNED_UMAP$UMAP_1 < 14.7 & UNASSIGNED_UMAP$UMAP_2 > -20 & UNASSIGNED_UMAP$UMAP_2 < 2 )
UNASSIGNED02 = subset(UNASSIGNED_Seu, idents = c(1))
UNASSIGNED02Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED02) & UNASSIGNED_UMAP$UMAP_1 < -10 & UNASSIGNED_UMAP$UMAP_2 > 3)
UNASSIGNED03 = subset(UNASSIGNED_Seu, idents = 2)
UNASSIGNED03Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED03) & UNASSIGNED_UMAP$UMAP_1 > -13 & UNASSIGNED_UMAP$UMAP_1 < 2 & UNASSIGNED_UMAP$UMAP_2 > -3 & UNASSIGNED_UMAP$UMAP_2 < 11)
UNASSIGNED04 = subset(UNASSIGNED_Seu, idents = c(3))
UNASSIGNED04Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED04) & UNASSIGNED_UMAP$UMAP_1 > -16 & UNASSIGNED_UMAP$UMAP_1 < -1 & UNASSIGNED_UMAP$UMAP_2 > -8 & UNASSIGNED_UMAP$UMAP_2 < 4)
UNASSIGNED05 = subset(UNASSIGNED_Seu, idents = 4)
UNASSIGNED05Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED05) & UNASSIGNED_UMAP$UMAP_1 > 3 & UNASSIGNED_UMAP$UMAP_1 < 15 & UNASSIGNED_UMAP$UMAP_2 > -2 & UNASSIGNED_UMAP$UMAP_2 < 11)
UNASSIGNED06 = subset(UNASSIGNED_Seu, idents = 5)
UNASSIGNED06Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED06) & UNASSIGNED_UMAP$UMAP_2 < -10)
UNASSIGNED07 = subset(UNASSIGNED_Seu, idents = 6)
UNASSIGNED07Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED07) & UNASSIGNED_UMAP$UMAP_1 > 5 & UNASSIGNED_UMAP$UMAP_1 < 16 & UNASSIGNED_UMAP$UMAP_2 > 9)
UNASSIGNED08 = subset(UNASSIGNED_Seu, idents = 7)
UNASSIGNED08Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED08) & UNASSIGNED_UMAP$UMAP_1 > 21.5 & UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < 2)
UNASSIGNED09 = subset(UNASSIGNED_Seu, idents = c(8))
UNASSIGNED09Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED09) & UNASSIGNED_UMAP$UMAP_1 < -5 & UNASSIGNED_UMAP$UMAP_2 > 0 & UNASSIGNED_UMAP$UMAP_2 < 14)
UNASSIGNED10 = subset(UNASSIGNED_Seu, idents = c(9))
UNASSIGNED10Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED10) & UNASSIGNED_UMAP$UMAP_2 > -23 & UNASSIGNED_UMAP$UMAP_1 < -12)
UNASSIGNED11 = subset(UNASSIGNED_Seu, idents = c(10,20))
UNASSIGNED11Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED11) & UNASSIGNED_UMAP$UMAP_1 > -11 & UNASSIGNED_UMAP$UMAP_1 < 1 & UNASSIGNED_UMAP$UMAP_2 > -20 & UNASSIGNED_UMAP$UMAP_2 < -5)
UNASSIGNED12 = subset(UNASSIGNED_Seu, idents = c(11))
UNASSIGNED12Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED12) & UNASSIGNED_UMAP$UMAP_1 > 15 & UNASSIGNED_UMAP$UMAP_2 > 0)
UNASSIGNED13 = subset(UNASSIGNED_Seu, idents = c(12, 18))
UNASSIGNED13Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED13) & UNASSIGNED_UMAP$UMAP_2 > 13)
UNASSIGNED14 = subset(UNASSIGNED_Seu, idents = c(13))
UNASSIGNED14Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED14) & UNASSIGNED_UMAP$UMAP_2 < -20)
UNASSIGNED15 = subset(UNASSIGNED_Seu, idents = c(14))
UNASSIGNED15Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED15) & UNASSIGNED_UMAP$UMAP_1 > 12 & UNASSIGNED_UMAP$UMAP_1 < 20 & UNASSIGNED_UMAP$UMAP_2 > -5 & UNASSIGNED_UMAP$UMAP_2 < 5)
UNASSIGNED16 = subset(UNASSIGNED_Seu, idents = c(15))
UNASSIGNED16Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED16) & UNASSIGNED_UMAP$UMAP_1 > 16 & UNASSIGNED_UMAP$UMAP_2 > 10)
UNASSIGNED17Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED16) & UNASSIGNED_UMAP$UMAP_2 < 10 & UNASSIGNED_UMAP$UMAP_1 < 20)
UNASSIGNED18 = subset(UNASSIGNED_Seu, idents = c(16))
UNASSIGNED18Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED18) & UNASSIGNED_UMAP$UMAP_2 > 5)
UNASSIGNED19 = subset(UNASSIGNED_Seu, idents = c(17))
UNASSIGNED20 = subset(UNASSIGNED_Seu, idents = c(19))
UNASSIGNED20Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED20) & UNASSIGNED_UMAP$UMAP_2 > -20)
UNASSIGNED21 = subset(UNASSIGNED_Seu, idents = c(20))
UNASSIGNED21Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED21) & UNASSIGNED_UMAP$UMAP_2 > 10 & UNASSIGNED_UMAP$UMAP_1 < 5)
UNASSIGNED22 = subset(UNASSIGNED_Seu, idents = c(21))
#CheckInput = UNASSIGNED11Clean
#CheckUMAP(UNASSIGNED_Seu)
UNASSIGNED_REST = subset(UNASSIGNED_Seu, cells = c(row.names(UNASSIGNED01Clean), row.names(UNASSIGNED02Clean), row.names(UNASSIGNED03Clean), row.names(UNASSIGNED04Clean), row.names(UNASSIGNED05Clean), row.names(UNASSIGNED06Clean), row.names(UNASSIGNED07Clean), row.names(UNASSIGNED08Clean), row.names(UNASSIGNED09Clean), row.names(UNASSIGNED10Clean), row.names(UNASSIGNED11Clean), row.names(UNASSIGNED12Clean), row.names(UNASSIGNED13Clean), row.names(UNASSIGNED14Clean), row.names(UNASSIGNED15Clean), row.names(UNASSIGNED16Clean), row.names(UNASSIGNED17Clean), row.names(UNASSIGNED18Clean), colnames(UNASSIGNED19), row.names(UNASSIGNED20Clean), row.names(UNASSIGNED21Clean), colnames(UNASSIGNED22)), invert=T)
#CheckInput = UNASSIGNED10_Extras
#CheckUMAP(UNASSIGNED_Seu)
UNASSIGNED02_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -15 & UNASSIGNED_UMAP$UMAP_2 > 0 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED02Clean))
UNASSIGNED03_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST)& UNASSIGNED_UMAP$UMAP_1 > -11 & UNASSIGNED_UMAP$UMAP_1 < 2 & UNASSIGNED_UMAP$UMAP_2 > -3 & UNASSIGNED_UMAP$UMAP_2 < 5 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED03Clean))
UNASSIGNED06_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 10 & UNASSIGNED_UMAP$UMAP_2 < -13 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED06Clean))
UNASSIGNED07_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > 10 & UNASSIGNED_UMAP$UMAP_1 > 5 & UNASSIGNED_UMAP$UMAP_1 < 16 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED07Clean))
UNASSIGNED08_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 20 & UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < 0.5 | UNASSIGNED_UMAP$UMAP_1 > 22 & UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < 1 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED08Clean))
UNASSIGNED10_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -23 & UNASSIGNED_UMAP$UMAP_2 < -14 & UNASSIGNED_UMAP$UMAP_1 < -12 | row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -15 & UNASSIGNED_UMAP$UMAP_2 < -10 & UNASSIGNED_UMAP$UMAP_1 < -12 & UNASSIGNED_UMAP$UMAP_1 > -20 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED10Clean))
UNASSIGNED11_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -13 & UNASSIGNED_UMAP$UMAP_1 < 0 & UNASSIGNED_UMAP$UMAP_2 > -20 & UNASSIGNED_UMAP$UMAP_2 < -8 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED10_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED11Clean))
UNASSIGNED12_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 18 & UNASSIGNED_UMAP$UMAP_2 > 0 & UNASSIGNED_UMAP$UMAP_2 < 12 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED08_Extras)| row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED12Clean))
UNASSIGNED14_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -8 & UNASSIGNED_UMAP$UMAP_2 < -20 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED10_Extras), row.names(UNASSIGNED11_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED14Clean))
UNASSIGNED15_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 12 & UNASSIGNED_UMAP$UMAP_1 < 20 & UNASSIGNED_UMAP$UMAP_2 > -5 & UNASSIGNED_UMAP$UMAP_2 < 4 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED12_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED15Clean))
UNASSIGNED16_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > 12 & UNASSIGNED_UMAP$UMAP_1 > 12 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED07_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED16Clean))
UNASSIGNED17_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) &UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < -6 & UNASSIGNED_UMAP$UMAP_1 > 13 & UNASSIGNED_UMAP$UMAP_1 < 20 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED17Clean))
UNASSIGNED18_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -6 & UNASSIGNED_UMAP$UMAP_1 < 3 & UNASSIGNED_UMAP$UMAP_2 < 12 & UNASSIGNED_UMAP$UMAP_2 > 0 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED03_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED18Clean))
UNASSIGNED19_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -4 & UNASSIGNED_UMAP$UMAP_1 < 7 & UNASSIGNED_UMAP$UMAP_2 < -19 | row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED19))
UNASSIGNED20_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -17 & UNASSIGNED_UMAP$UMAP_2 > -16 & UNASSIGNED_UMAP$UMAP_2 < 5 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED10_Extras), row.names(UNASSIGNED02_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED20Clean))
UNASSIGNED04_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -9 & UNASSIGNED_UMAP$UMAP_2 < 4 & UNASSIGNED_UMAP$UMAP_1 > -18 & UNASSIGNED_UMAP$UMAP_1 < -3 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED03_Extras), row.names(UNASSIGNED11_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED04Clean))
UNASSIGNED09_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -6 & UNASSIGNED_UMAP$UMAP_2 < 14 & UNASSIGNED_UMAP$UMAP_2 > 3 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED02_Extras), row.names(UNASSIGNED03_Extras), row.names(UNASSIGNED04_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED09Clean))
UNASSIGNED13_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -15 & UNASSIGNED_UMAP$UMAP_1 < -1 & UNASSIGNED_UMAP$UMAP_2 > 12 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED09_Extras) | UNASSIGNED_UMAP$UMAP_1 > -1 & UNASSIGNED_UMAP$UMAP_1 < 5 & UNASSIGNED_UMAP$UMAP_2 > 18 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED09_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED13Clean))
UNASSIGNED21_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -5 & UNASSIGNED_UMAP$UMAP_1 < 5 & UNASSIGNED_UMAP$UMAP_2 > 12 & UNASSIGNED_UMAP$UMAP_2 < 20 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED13_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED21Clean))
UNASSIGNED05_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -1 & UNASSIGNED_UMAP$UMAP_2 < 11 & UNASSIGNED_UMAP$UMAP_1 > 3 & UNASSIGNED_UMAP$UMAP_1 < 15 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED15_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED05Clean))
UNASSIGNED01_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -3 & UNASSIGNED_UMAP$UMAP_1 < 17 & UNASSIGNED_UMAP$UMAP_2 > -23 & UNASSIGNED_UMAP$UMAP_2 < 4 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED19_Extras), row.names(UNASSIGNED17_Extras), row.names(UNASSIGNED15_Extras), row.names(UNASSIGNED11_Extras), row.names(UNASSIGNED05_Extras), row.names(UNASSIGNED06_Extras), row.names(UNASSIGNED03_Extras), row.names(UNASSIGNED18_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED01Clean))
UNASSIGNED22_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -5 & UNASSIGNED_UMAP$UMAP_1 > -10 & UNASSIGNED_UMAP$UMAP_2 > 0 & UNASSIGNED_UMAP$UMAP_2 < -10 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED04_Extras) | row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED22))
#CheckInput = UNASSIGNED10_Extras
#CheckUMAP(UNASSIGNED_Seu)
UNASSIGNED_Assigns = GenerateMetaData_Barcodes(list("UNASSIGNED_01" = UNASSIGNED01_Extras, "UNASSIGNED_02" = UNASSIGNED02_Extras,"UNASSIGNED_03" = UNASSIGNED03_Extras, "UNASSIGNED_04" = UNASSIGNED04_Extras, "UNASSIGNED_05" = UNASSIGNED05_Extras, "UNASSIGNED_06" = UNASSIGNED06_Extras, "UNASSIGNED_07" = UNASSIGNED07_Extras, "UNASSIGNED_08" = UNASSIGNED08_Extras, "UNASSIGNED_09" = UNASSIGNED09_Extras, "UNASSIGNED_10" = UNASSIGNED10_Extras, "UNASSIGNED_11" = UNASSIGNED11_Extras, "UNASSIGNED_12" = UNASSIGNED12_Extras,"UNASSIGNED_13" = UNASSIGNED13_Extras, "UNASSIGNED_14" = UNASSIGNED14_Extras, "UNASSIGNED_15" = UNASSIGNED15_Extras, "UNASSIGNED_16" = UNASSIGNED16_Extras, "UNASSIGNED_17" = UNASSIGNED17_Extras, "UNASSIGNED_18" = UNASSIGNED18_Extras, "UNASSIGNED_19" = UNASSIGNED19_Extras,"UNASSIGNED_20" = UNASSIGNED20_Extras, "UNASSIGNED_21" = UNASSIGNED21_Extras, "UNASSIGNED_22" = UNASSIGNED22_Extras))
UNASSIGNED_01_Rest = subset(UNASSIGNED_Seu, cells = UNASSIGNED_Assigns$Barcodes, invert=T)
UNASSIGNED_Assigns = GenerateMetaData(list("UNASSIGNED_01" = UNASSIGNED01_Extras, "UNASSIGNED_02" = UNASSIGNED02_Extras,"UNASSIGNED_03" = UNASSIGNED03_Extras, "UNASSIGNED_04" = UNASSIGNED04_Extras, "UNASSIGNED_05" = UNASSIGNED05_Extras, "UNASSIGNED_06" = UNASSIGNED06_Extras, "UNASSIGNED_07" = UNASSIGNED07_Extras, "UNASSIGNED_08" = UNASSIGNED08_Extras, "UNASSIGNED_09" = UNASSIGNED09_Extras, "UNASSIGNED_10" = UNASSIGNED10_Extras, "UNASSIGNED_11" = UNASSIGNED11_Extras, "UNASSIGNED_12" = UNASSIGNED12_Extras,"UNASSIGNED_13" = UNASSIGNED13_Extras, "UNASSIGNED_14" = UNASSIGNED14_Extras, "UNASSIGNED_15" = UNASSIGNED15_Extras, "UNASSIGNED_16" = UNASSIGNED16_Extras, "UNASSIGNED_17" = UNASSIGNED17_Extras, "UNASSIGNED_18" = UNASSIGNED18_Extras, "UNASSIGNED_19" = UNASSIGNED19_Extras,"UNASSIGNED_20" = UNASSIGNED20_Extras, "UNASSIGNED_21" = UNASSIGNED21_Extras, "UNASSIGNED_22" = UNASSIGNED22_Extras)) #, "UNASSIGNED_01" = UNASSIGNED_01_Rest))
UNASSIGNED_Assigns$Dups = duplicated(UNASSIGNED_Assigns$Barcodes) | duplicated(UNASSIGNED_Assigns$Barcodes, fromLast=T)
UNASSIGNED_Assigns_T = subset(UNASSIGNED_Assigns, UNASSIGNED_Assigns$Dups == T)
unique(UNASSIGNED_Assigns_T$Pop)
#CheckInput = UNASSIGNED01_Extras
#CheckUMAP(UNASSIGNED_Seu)
save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns", "AH_Seu", "AH_Assigns", "MN_Seu", "MN_Assigns", "UNASSIGNED_Seu", "UNASSIGNED_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
-
Output subclustering
SeuList = list("ARC" = ARC_Seu, "PVH" = PVH_Seu, "VMH" = VMH_Seu,"SMN" = SMN_Seu, "LH" = LH_Seu, "TM" = TM_Seu, "SCN" = SCN_Seu, "DMH" = DMH_Seu, "PO" = PO_Seu, "AH" = AH_Seu, "MN" = MN_Seu, "UNASSIGNED" = UNASSIGNED_Seu, "Microglia" = Microglia_Seu, "VLMC" = VLMC_Seu, "Pericytes" = Pericytes_Seu, "SMC" = SMC_Seu, "Radial Glia" = RG_Seu, "Neurepithelial" = NE_Seu, "Dividing Progenitors" = Div_Seu, "Intermediate Progenitors" = IP_Seu, "Astrocytes" = Astro_Seu, "Oligodendrocytes" = Olig_Seu, "Ependymocytes" = Ependy_Seu, "Tanycytes" = Tany_Seu, "Endothelial" = Endo_Seu)
AssignsList = list("ARC" = ARC_Assigns, "PVH" = PVH_Assigns, "VMH" = VMH_Assigns,"SMN" = SMN_Assigns, "LH" = LH_Assigns, "TM" = TM_Assigns, "SCN" = SCN_Assigns, "DMH" = DMH_Assigns, "PO" = PO_Assigns, "AH" = AH_Assigns, "MN" = MN_Assigns, "UNASSIGNED" = UNASSIGNED_Assigns, "Microglia" = Microglia_Assigns, "VLMC" = VLMC_Assigns, "Pericytes" = Pericytes_Assigns, "SMC" = SMC_Assigns, "Radial Glia" = RG_Assigns, "Neurepithelial" = NE_Assigns, "Dividing Progenitors" = Div_Assigns, "Intermediate Progenitors" = IP_Assigns, "Astrocytes" = Astro_Assigns, "Oligodendrocytes" = Olig_Assigns, "Ependymocytes" = Ependy_Assigns, "Tanycytes" = Tany_Assigns, "Endothelial" = Endo_Assigns)
#Check all assigns are equal to number of cells
for(x in names(AssignsList)){
cat(paste(x, dim(AssignsList[[x]])[1] - dim(SeuList[[x]])[2], "\n"))
}
#Count Clusters
NCells = 0
AllAssigns = as.data.frame(matrix(ncol=1, nrow=0))
colnames(AllAssigns) = "Pop"
for(x in names(AssignsList)){
NCells = NCells+length(unique(AssignsList[[x]]$Pop) )
AllAssigns = rbind(AllAssigns, AssignsList[[x]])
}
#Generate a list of new assignments - includes Class and Subclass annotations - used in Glowworm Mapping
Hypo_Assignments = as.data.frame(matrix(ncol=2, nrow=0))
colnames(Hypo_Assignments) = c("Class", "Subclass")
for(x in names(AssignsList)){
PullAssigns = AssignsList[[x]]
colnames(PullAssigns) = "Subclass"
PullAssigns$Class = ifelse(x %in% c("ARC", "PVH", "VMH", "SMN", "LH", "TM", "SCN", "DMH", "PO", "AH", "MN", "UNASSIGNED"), "Neurons", x)
#PullAssigns$Subclass = ifelse(x %in% "UNASSIGNED", "Unassigned", PullAssigns$Subclass)
PullAssigns$Class = ifelse(PullAssigns$Subclass %in% grep(pattern = "^RG",x = PullAssigns$Subclass, value = T), "Radial Glia", PullAssigns$Class)
Hypo_Assignments = rbind(Hypo_Assignments, PullAssigns)
}
Hypo_Assignments$Subclass = gsub("UNASSIGNED", "Ukn", Hypo_Assignments$Subclass)
write.csv(Hypo_Assignments, "~/Library/CloudStorage/Box-Box/HG2553 Main Folder/Hypo_Assignments_1MAY23.csv")
#Output all files - single page on one pds
OutPlot = list()
for(x in names(SeuList)){
GetSeu = SeuList[[x]]
GetMeta = AssignsList[[x]]
GetSeu = AddMetaData(GetSeu, GetMeta, "Assigns")
Idents(GetSeu) = "Assigns"
OutPlot[[x]] = DimPlot(GetSeu, label=T)+NoLegend()+ggtitle(x)
#Idents(GetSeu) = "SampleAdult"
#OutPlot[[paste(x, "samples")]] = DimPlot(GetSeu, label=T)+NoLegend()+ggtitle(x)
}
pdf("CluseteredPlots_Hypothalamus_1MAY23.pdf", width = 8, height = 8)
print(OutPlot)
dev.off()
#CluseteredPlots_Hypothalamus_5APR23.pdf
#Output plots - all on one page
OutPlot = list()
for(x in c("Radial Glia", "Neurepithelial" ,"Dividing Progenitors", "Intermediate Progenitors", "Astrocytes", "Oligodendrocytes", "Microglia", "VLMC", "Pericytes" ,"SMC", "TM", "ARC", "PVH", "VMH", "LH", "DMH", "SCN", "PO", "AH", "SMN", "MN", "UNASSIGNED")){
Title = ifelse(x %in% c("ARC", "PVH", "VMH", "SMN", "LH", "TM", "SCN", "DMH", "PO", "AH", "MN"), paste("Neurons:", x), x)
Title = ifelse(x %in% "UNASSIGNED", "Neurons: Unassigned", Title)
GetSeu = SeuList[[x]]
GetMeta = AssignsList[[x]]
GetSeu = AddMetaData(GetSeu, GetMeta, "Assigns")
Idents(GetSeu) = "Assigns"
OutPlot[[x]] = DimPlot(GetSeu, label=F)+NoLegend()+ggtitle(Title) + theme(axis.line = element_line(color = "lightgrey"), axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(), plot.title = element_text(size = 10))
}
Patch = wrap_plots(OutPlot) +
plot_layout(design = "##ABCD
##EFGH
IJKLMN
OPQRST
UV####")
pdf("Fetal_Figure_28APR22.pdf", width = 12, height = 12)
print(Patch)
dev.off()
#3D UMAP - main annotations
CellPopColors = c("Neurepithelial"= "#aad576", "Dividing Progenitors"= "#78a02d", "Radial Glia" = "#538d22", "Intermediate Progenitors" = "#245501", "Oligodendrocytes"= "#59a5d8", "Neurons"= "#7251b5", "Astrocytes" = "#ff7aa2", "Ependymocytes" = "#ff9ebb", "Tanycytes" = "#ffc2d4", "Microglia" = "#d64050", "Pericytes" = "#fecf29" , "SMC" ="#fee08b", "VLMC" = "#faad60", "Endothelial" = "#f36c44", "Blood" ="#9e1b44") #19
CleanedClusters_Figure1 = read.csv("~/Dropbox/Columbia/Brian Hypothalamus/ScienceAdvances_2023/Figure 1/CleanedClusters_Figure1_19DEC22.csv", row.names =1)
CleanedClusters_Figure1 = merge(CleanedClusters_Figure1, Hypo_Assignments, by.x = "Row.names", by.y = 0)
CleanedClusters_Figure1 = subset(CleanedClusters_Figure1, CleanedClusters_Figure1$Row.names %in% colnames(AllHypo))
p = plot_ly(CleanedClusters_Figure1, x = ~UMAP_3, y = ~UMAP_2, z = ~UMAP_1, size = 1, color = ~Class, colors = CellPopColors, type="scatter3d")
htmlwidgets::saveWidget(p, "HYPOTHALAMUS_AllAssignments_GLOWWORM.html")
Hannahglover/Glowworm documentation built on Jan. 16, 2024, 11:47 p.m.
R Package Documentation
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Setup
library(Seurat) library(tidyverse) library(ggpubr) library(harmony) ClusterFunc_All_RNA = function(SeuFile){ Filename = as.character(substitute(SeuFile)) for(y in set.kparam){ for(z in set.dim){ for(v in set.res){ DefaultAssay(SeuFile) = "integrated" SeuFile <- FindNeighbors(SeuFile, k.param=y, dims=1:z) SeuFile <- FindClusters(SeuFile, resolution = v) DefaultAssay(SeuFile) = "RNA" pdf(paste("ISLETS_", Filename, "_res", v, "_k", y, "_dim", z, "_umapSML.pdf", sep=""), width=12, height=10) dimplot = DimPlot(SeuFile, reduction="umap", label=T) print(dimplot) dev.off() pdf(paste("ISLETS_", Filename, "_res", v, "_k", y, "_dim", z, "_umapLGE.pdf", sep=""), width=25, height=25) dimplot = DimPlot(SeuFile, reduction="umap", label=T) print(dimplot) dev.off() GeneLists2 = GeneLists for(GL in names(GeneLists2)){ GeneLists2[[GL]] = subset(GeneLists2[[GL]], GeneLists2[[GL]] %in% row.names(SeuFile)) } ###VLNS AllVlns = list() for(d in names(GeneLists2)){ Genes = GeneLists2[[d]] StorePlots = list() for(x in Genes[1]){ plotA <- VlnPlot(SeuFile, features = x, pt.size = 0, same.y.lims = F,) plotA <- plotA + coord_flip()+ theme(axis.ticks.x= element_blank(), axis.title.y=element_blank(), axis.text.x=element_blank(), axis.title.x=element_blank(), axis.ticks.y = element_blank(), legend.position = "none", plot.title = element_text(size=12))+ labs(title = d, subtitle = Genes[1]) StorePlots[[x]] = plotA } for(x in Genes[2:length(Genes)]){ plotB <- VlnPlot(SeuFile, features = x, pt.size = 0, same.y.lims = F,) plotB <- plotB + coord_flip()+ theme(axis.ticks.x= element_blank(), axis.title.y=element_blank(), axis.text.x=element_blank(), axis.title.x=element_blank(), axis.ticks.y = element_blank(), legend.position = "none", axis.text.y = element_blank(), plot.title = element_text(size=12)) StorePlots[[x]] = plotB } AllVlns[[d]] <- ggarrange(plotlist = StorePlots, widths=c(1.4, rep(1, length(Genes)-1)), ncol = 60, nrow = 1) } pdf(paste("ISLETS_", Filename, "_res", v, "_k", y, "_dim", z, "_AllMultiVlns.pdf", sep=""), width=60, height=length(unique(SeuFile@active.ident))) print(AllVlns) dev.off() #Cell No CellNo = as.data.frame(table(SeuFile@meta.data$seurat_clusters)) write.csv(CellNo, paste("ISLETS_", Filename, "_counts_k", y, "_dim", z, "_res", v, ".csv", sep=""), row.names = F) }} #Feature Plots FPList = list() for(d in names(GeneLists2)){ Genes = GeneLists2[[d]] FPSinglePage = list() FPSinglePage[[1]] = FeaturePlot(SeuFile, Genes[1], reduction="umap") + labs(title=paste(d, Genes[1])) for(p in seq(2, length(Genes), 1)){ FPSinglePage[[p]] = FeaturePlot(SeuFile, Genes[p], reduction="umap") } FPList[[d]] = ggarrange(plotlist = FPSinglePage, ncol=6, nrow=10) } pdf(paste("ISLETS_", Filename, "_dim", z, "_AllFPs.pdf", sep=""), width = 25, height = 45) print(FPList) dev.off() }} CheckUMAP = function(SeuFilez){ if(class(CheckInput) == "data.frame"){ CheckMeta = as.data.frame(c(rep("RoI", length(row.names(CheckInput))))) colnames(CheckMeta) = "Pop" row.names(CheckMeta) = row.names(CheckInput) }else{ CheckMeta = as.data.frame(c(rep("RoI", length(colnames(CheckInput))))) colnames(CheckMeta) = "Pop" row.names(CheckMeta) = colnames(CheckInput) } SeuFilez = AddMetaData(SeuFilez, CheckMeta, "CheckMeta") Idents(SeuFilez) = "CheckMeta" DimPlot(SeuFilez, reduction="umap") } ######################################################################## GenerateMetaData = function(ListMeta){ MetaOutput = as.data.frame(matrix(ncol = 1, nrow =0)) for(x in seq(1,length(ListMeta),1)){ if(class(ListMeta[[x]]) == "data.frame"){ Temp = as.data.frame(rep(names(ListMeta)[[x]], length(row.names(ListMeta[[x]])))) colnames(Temp) = "Pop" row.names(Temp) = row.names(ListMeta[[x]]) }else{ Temp = as.data.frame(rep(names(ListMeta)[[x]], length(colnames(ListMeta[[x]])))) colnames(Temp) = "Pop" row.names(Temp) = colnames(ListMeta[[x]]) } MetaOutput = rbind(MetaOutput, Temp) } return(MetaOutput) } GenerateMetaData_Barcodes = function(ListMeta){ MetaOutput = as.data.frame(matrix(ncol = 2, nrow =0)) for(x in seq(1,length(ListMeta),1)){ if(class(ListMeta[[x]]) == "data.frame"){ Temp = as.data.frame(rep(names(ListMeta)[[x]], length(row.names(ListMeta[[x]])))) colnames(Temp) = "Pop" Temp$Barcodes = row.names(ListMeta[[x]]) }else{ Temp = as.data.frame(rep(names(ListMeta)[[x]], length(colnames(ListMeta[[x]])))) colnames(Temp) = "Pop" Temp$Barcodes = colnames(ListMeta[[x]]) } MetaOutput = rbind(MetaOutput, Temp) } return(MetaOutput) }
Gene Lists: All Pops
GeneLists = list() GeneLists[["Zhou_MainMarkers"]] = c("POMC", "VIM", "HES1", "HMGA2", "ARHGAP28", "NES", "ASCL1", "NBPF10", "NBPF15", "PLAGL1", "SFTA3", "STMN2", "SYT1", "SLC32A1", "GAD2", "HDC", "SLC17A6", "PDGFRA", "APOD", "GPR75-ASB3", "MBP", "MOG", "AQP4", "AGT", "FAM107A", "CCDC153", "CCDC74A", "CCDC74B", "LRTOMT", "AIF1", "CX3CR1", "CLDN5", "FLT1", "SLC38A5", "NDUFA4L2", "PDGFRB", "PTGDS", "COL1A1") GeneLists[["Zhou_MainMarkersV2"]] = c("OLIG1", "OLIG2", "SOX8", "JUNB", "EGR1", "MYC", "OLIG1","OLIG2", "NKX2-2", "FOXJ1", "GLIS3", "AXNA1","TBX2", "TBX3", "LHX2", "RAX", "SHH", "SIX3", "SIX6", "SLC1A3", "NES", "FABP7", "FAM107A", "CD82","TBX3", "RAX", "SIX3", "OTX2", "IRX2", "IRX3", "IRX5", "DLX1", "DLX2", "DLX5", "DLX6", "SOX3", "LHX1", "ONECUT2", "OTP") GeneLists[["BroadGenes"]] = c("SYT1", "SNAP25", "TUBB3","SYP", #Broad Neuronal "SLC32A1", "DLX1", "DLX2", "DLX5", "GAD1", #GABAergic "SLC17A6", "SLC17A8") #Glutaminergic GeneLists[["HypothalamicProgenSubclass"]] =c("VIM", "ASCL1", #Hypothalamic Progenitor - And/or "HES1", "MKI67", "FABP7", "TTYH1", "HMGA2", "MEIS2", "SLC1A3", "FAM107A", "NKX2-1", "NHLH2", "DLX1", "OLIG2", "DLX6-AS1", "STMN2", "HOPX", "HES1", "NES", "SOX2", "EGFR", "DLL1", "CLDN10", "FAM107A", "GADD45G", "HES6", "SLC1A2") #Hypothalamic subclasses GeneLists[["OligList"]] = unique(c("APOD", "PMP22", "MAG", "OLIG1", "PLLP", "CSPG4", "PDGFRA", "NEU4", "TNS3", "FYN", "TCF7L2", "MAL", "MOG", "PLP1", "SERINC5", "TFRC", "OLIG1", "OLIG2", "CNP", "ST8SIA1", "CD9")) GeneLists[["AstroMicro"]] = unique(c("GJA1", "GFAP", "AQP4", "SLC7A10", "ALDH1L1", "AGT", "SLC1A3", "S100B", "ALDOC", "SLC1A2", "ID3", "ASCL1", "BTG2", "APOE", "C1QB", "AIF1", "P2RY12", "MRC1")) GeneLists[["EpendyTanyList"]] = c("FOXJ1", "RAX", "ADM", "EMX2", "LHX2", "TMEM212", "CRYM", "CCDC153", "RFX2", "RFX3", "RFX4", "NPAS3", "COL23A1", "CD59", "SLC17A8", "VCAN", "FRZB", "PENK", "COL25A1", "CACNA2D2", "STOML3", "TMEM212", "SLC7A11", "PCP4L1", "PLTP", "TGFB2", "NR2E1", "EPHB1", "P3H2", "NELL2", "FGF10", "RGCC", "GRIA2", "RLBP1", "SIX6", "SCN7A", "MEST", "IGFBP5", "RGS7BP") GeneLists[["EndoList"]] = c("SLCO1C1", "SLC38A5", "MYH11", "MRC1", "CLDN5", "ITM2A", "FLT1") GeneLists[["MuralPeriVLMCList"]] = c("VTN", "COL1A1", "COL3A1", "LUM", "DCN", "PTGDS", "AMBP", "RGS5", "ACTA2", "TAGLN", "ABCC9", "KCNJ8") #3 GenMural, 6 VLMC, 2 Peri, 1 SMA GeneLists[["NeuronsBroad"]] = c("SYT1", "SNAP25", "TUBB3","SYP", #Broad Neuronal "SLC32A1", "DLX1", "DLX2", "DLX5", "GAD1", #GABAergic "SLC17A6", "SLC17A8", #Glutaminergic "SST", "TH", "TRH","BDNF", "OTP","PCSK1") #Pan regional neuropepetides or functional genes GeneLists[["Zeisel_Vasculature"]] = toupper(c("Cldn5", "Adgrf5", "Emcn", "Vtn", "Cspg4", "Atp13a5", "Pth1r", "Kcnj8", "Abcc9", "Apln", "Cd82", "Chst1", "Tagln", "Pln", "Bmx", "Gkn3", "Igfbp2", "Dcn", "Lum", "Pdgfra", "Il33", "Ptgds", "Nnat", "Rspo3", "Nov", "Slc47a1", "Sox10", "Foxd3", "Aldh1a3", "Anxa11", "Slc18a2", "Klhl30", "Gfra3", "Cldn19", "Mpz", "Dhrs2", "Caecam10", "Cspg5", "Olig1", "Pcdh15", "Aldoc", "Npy", "Apod")) GeneLists2 = GeneLists for(GL in names(GeneLists2)){ GeneLists2[[GL]] = subset(GeneLists2[[GL]], GeneLists2[[GL]] %in% row.names(KaZhouAll)) }
Gene Lists: Neurons
GeneLists = list() GeneLists[["NeuronsBroad"]] = c("SYT1", "SNAP25", "TUBB3","SYP", #Broad Neuronal "SLC32A1", "DLX1", "DLX2", "DLX5", "GAD1", #GABAergic "SLC17A6", "SLC17A8", #Glutaminergic "SST", "TH", "TRH","BDNF", "OTP","PCSK1") #Pan regional neuropepetides or functional genes GeneLists[["NeuronsARC"]] = c("NKX2-1", "KISS1", "GHRH", "POMC","NPY","AGRP", "HDC", "TBX3", "ISL1", "LEPR", "NPY1R", "CRABP1", "HMX2","GSX1") #HDC+ plus arcuate markers is the tuberomammullary terminal GeneLists[["NeuronsPVH"]] = c("SIM1", "POU3F2", "NHLH2", "MC4R", "OXT", "AVP", "CRH", "GAL") GeneLists[["NeuronsVMH"]] = c("GABRA5", "NPTX2", "FEZF1", "NR5A1") GeneLists[["NeuronsMN"]] = c("PITX2", "FOXP1", "FOXP2", "FOXB1", "SOX14", "FEZF2", "LHX1") GeneLists[["NeuronsLH"]] = c("HCRT", "CARTPT", "PMCH", "LHX2", "LHX9", "PDYN") GeneLists[["NeuronsSCN"]] = c("RGS16","LHX1", "GRP", "VIP", "CCK", "VAX1", "SIX3", "SIX6") GeneLists[["NeuronsSMN"]] = c("BARHL1", "FOXA1", "LMX1A", "IRX3", "IRX5") GeneLists[["NeuronsDMH"]] = c("PPP1R17", "NKX2-2", "LHX8") GeneLists[["NeuronsPO"]] = c("NR2F1", "NR2F2", "HMX3", "RELN") CompileNeurons = unique(c(GeneLists[["NeuronsBroad"]], GeneLists[["NeuronsARC"]], GeneLists[["NeuronsPVH"]], GeneLists[["NeuronsVMH"]], GeneLists[["NeuronsMN"]], GeneLists[["NeuronsLH"]], GeneLists[["NeuronsSCN"]], GeneLists[["NeuronsSMN"]], GeneLists[["NeuronsDMH"]], GeneLists[["NeuronsPO"]])) GeneLists[["Campbell_ARC"]] =unique(toupper(c("Hdc", "Gm8773", "Tac1", "Th", "Sst", "Sst", "Nts", "Nfix", "Htr2c", "Oxt", "Arx", "Nr5a2", "Th", "Slc6a3", "Th", "Nfib", "Ghrh", "Trh", "Cxcl12", "Agrp", "Sst", "Agrp", "Gm8773", "Pomc", "Ttr", "Pomc", "Anxa2", "Rgs16", "Vip", "Rgs16", "Dlx1", "Rgs16", "Nmu", "Gpr50", "Kiss1", "Tac2", "Pomc", "Glipr1", "Tmem215", "Sst", "Unc13c", "Sst", "Pthlh", "Trh", "Lef1", "Htr3b", "Tbx19", "Qrfp", "Nr5a1", "Bdnf", "Nr5a1", "Nfib", "Slc17a6", "Fam19a2", "Slc17a6", "Trhr"))) GeneLists[["Kim_VMH"]] =unique(toupper(c("Lhx1", "Slc18a3", "Lhx9", "Clic4", "Tbx3", "Tac2", "Anxa2", "Slc14a1", "Slc32a1", "Nup62cl", "Irx5", "Slc32a1", "Nfib", "Prdm13", "Car4", "Nr5a1", "Foxp2", "Prdm13", "Foxp2", "C1ql2", "Foxp2", "Tsix", "Esr1", "Gabrg3", "Esr1", "Twist2", "Esr1", "Insm2", "Samd9l", "Rorb", "Nmur2", "Smoc2", "Ngf", "Ctxn3", "Nup62cl", "Satb2", "Calcrl", "Six3", "Dlk1", "ll1rapl2", "F2rl2", "Nts", "Maob", "Adarb2", "Egflam", "Scgn", "Npffr2", "Glipr1", "Tcf712", "Ctxn3", "Car8", "Rorb"))) GeneLists[["Moffitt_PO"]] =unique(toupper(c("Tac1", "Fezf1", "Cartpt", "Isl1", "Trh", "Angpt1", "Adcyap1", "Nkx2-1", "Trp73", "Reln", "C1ql1", "Cck", "Ebf3", "Tcf7l2","Meis2", "Shox2", "Foxp2", "Ghrh", "C1ql1", "Ucn3", "Brs3", "Th", "Crh", "Rxpf3", "Gal", "Etv1", "Rxfp1", "Slc17a6", "Nts", "Slc17a8", "Slc32a1", "Gsc", "Pdyn", "Mylk", "Pou3f3", "Nms", "Amigo2", "Avp", "Cck", "Th", "Nos1", "Tac2", "Moxd1", "Sst", "Pmaip1", "Nmu", "Prok2", "Six6", "Igsf1", "Vip", "Calca", "Npy", "Bdnf", "Chrm2", "Kiss1", "Penk", "Pthlh", "Chat", "Cxcl14"))) GeneLists[["Mickelsen_LH"]] =unique(toupper(c("Pmch", "Gad1", "Nrgn", "Gda", "Zic1", "Tac1", "Pitx2", "Ebf3", "Otp", "Hcrt", "Tcf4", "Trh", "Cbln2", "Synpr", "Grp", "Cck", "Calca", "Col27a1", "Syt2", "Meis2", "Gpr101", "Sst", "Gal", "Dlk1", "Npy", "Npw", "Nts", "Cartpt", "Calb2", "Col25a1", "Tac2", "Serpini1", "Lhx6", "Calb1", "Th", "Slc18a2", "Atp1a2", "Cbln4"))) GeneLists[["Kim_WholeHypo"]] =unique(toupper(c("Oxt", "Avp", "Cartpt", "Isl1", "Meis2", "Gal", "Pax6", "Cck", "Hdc", "Lhx8", "Ghrh", "Cited1", "Six6", "Hmx2", "Agrp", "Npy", "Pomc", "Tac1", "Sst", "Onecut2", "Calb2", "Pnoc", "Foxp2", "Nr2f2", "Zfhx3", "Calb1", "Foxb1", "Lhx1", "Penk", "Hcrt", "Lhx9", "Nts", "Lhx2", "Gnrh", "Kiss1", "Meis3", "Pbx1", "Pbx3", "Six3", "Th", "Vip", "Chchd10", "Fezf1", "Grp", "Pmch", "Nr4a2", "Foxp1", "Lhx5", "Lmx1a", "Nhlh2", "Crh", "Arx", "Crh", "Otp"))) GeneLists[["Mickelsen_VPH"]] =unique(toupper(c("Tac2", "Pvalb", "Fgf1", "Cabp7", "Foxb1", "Cck", "Nts", "Col25a1", "Gpr83", "Spock3", "Slc24a2", "Nos1", "Calb1", "Cxcl14", "Synpr", "Stxbp6", "Tcf4", "Nr4a2", "Ebf3", "Slc6a1", "Tac1", "Foxp2", "Glra3", "Htr2c", "Hdc", "Slc18a2", "Wif1", "Prph", "Nrxn3", "Thrb", "Rxrg", "Unc13c", "Pdyn", "Kiss1", "Esr1", "Agrp", "Npy", "Otp", "Dlk1", "Six3", "Sst", "Pthlh", "Ptk2b", "Nnat", "Epha5", "Calcr", "Cplx1", "Hspa41", "Kcnc1", "Gabre", "Gad2", "Asb4"))) GeneLists[["Wen_Morris_SCN"]] =unique(toupper(c("Avp", "Nms", "Cck", "Grp", "Ppp1r17", "Gem", "Vip", "C1ql3", "Synpr", "Prok2", "Cck", "Prokr2", "Mef2c", "Peg10", "Gadd45a", "Hspa1a", "Vipr2", "Lbh", "Rasd1", "Igfbp5", "Ptp4a1", "Aip", "Pkib", "Chodl", "Sncb", "Nrxn3", "Alcam"))) GeneLists[["Affinati_VMH"]] =unique(toupper(c("Dlk1", "Drd3", "Pdgfd", "Hnf4g", "Col2a1", "Ivl", "Cdh20", "Col12a1", "Pdzrn3", "Bmp4", "Smoc2", "Robo3", "Esr1", "Dkk1", "Gpr174", "Fli1", "Nfia", "Duox2", "Eya1", "Eya4", "Nfib", "Adamts19", "Slc17a8", "Tacr1", "Exph5", "Cntnap3", "Olfr920", "Col5a2", "Atf7ip2", "Itih5", "Foxp2", "Lpar3", "Aass", "Col24a1", "Npffr2", "Dppa5a", "Hgt", "Adgr94", "Igf1", "Fezf1", "Nmu", "Rorb", "Efemp1", "Lepr", "Bmp15", "Zfp968", "Dlx60s1", "Parp4", "Acp7", "Cobil1", "Slc38a11","Glis3", "Crhr2", "St18", "Unc45b", "Fam83b", "Tll2", "Tll6", "Pantr1", "Acvr1c", "Barx2", "Zar1", "Adamts9", "Slc22a3", "Fign", "Tcf7l2", "Ankrd33b"))) GeneLists[["Zhou_WholeHypo"]] =unique(c("ONECUT1", "ONECUT2", "OTP", "SIM1", "SIX3", "DLX1", "DLX2", "DLX5", "DLX6", "ISL1", "MEIS2", "SST", "DLK1", "ASCL1", "TBX3", "ACHE", "FEZF1", "NPTX2", "GHRH", "GSX1", "PCP4", "AGRP", "NPY", "NPY1R", "POMC", "CBLN1", "KISS1", "TAC3", "PDYN", "HCRT", "CRABP1", "NHLH1", "CRABP2", "IRX1", "IRX4", "ADCYAP1", "SSTR2", "HDC", "SLC18A2", "GULP1", "CBLN4", "LHX6", "ARX", "CALB2","PITX2", "LHX5", "SIM1", "CALB2", "BARHL1", "BARHL2", "IRX2", "IRX3", "IRX5", "NEUROD2", "TBR1", "EOMES", "EMX2", "SP9", "GBX2", "TCF7L2", "GATA2", "GATA3")) GeneLists[["Hypomap_ARC_PVH"]] =unique(toupper(c("Serpina3n", "Npy", "Agrp", "Acvr1c", "Npy", "Agrp", "Vcan", "Col6a1", "Il1rapl2", "Ptprk", "Npy", "Sst", "Il1rapl2", "Otp", "Sst", "Gpr101", "Otp", "Sst", "Crabp1", "Sytl4", "Tbx3", "Pou6f2", "Sytl4", "Tbx3", "Lepr", "Agrp", "Gpc3", "Bace2", "Epha3", "Il1rapl2", "Trh", "Nkx2-4", "Gpr50", "Nts", "Anxa2", "Pomc", "Glipr1", "Pomc", "Ttr", "Pomc", "Grp", "Ppp1r17", "Npvf", "Rfx4", "Qrfp", "Rfx4", "Nfix", "Satb2", "Slc6a3", "Satb2", "Nr5a2", "Satb2", "Nts", "Sst", "Pou3f1", "Sst", "Vgll3", "Tbx3", "Prdm12", "Tcf4", "Ghrh", "Lpar1", "Tac2", "Shox2", "C2cd4b", "Fam122b", "Vipr2", "Igfbpl1", "Cbln2", "Trh", "Gsc", "Sncg", "Ebf1", "Ucn3", "Crh", "Tent5a", "Ebf3", "Caprin2", "Oxt"))) GeneLists[["Hypomap_MN_PO"]] =unique(toupper(c("Reln", "Lef1", "Hdc", "Pde11a", "Foxb1", "Ebf3", "Ebf3", "Lmx1a", "Rxfp1", "Pitx2", "Slc6a3", "Nts", "Irx5", "Rxfp1", "Nts", "Myo5b", "Postn", "Nid1", "Postn", "Col19a1", "Fign", "Eya4", "Fign", "Nfia", "Nxph4", "Epha8", "Prkch", "Igfbp4", "Lhx6", "Sfrp1", "Sox6", "Lhx8", "Slc17a8", "Ppp1r1b", "Bcl11b", "Meis2", "Ccn1", "Meis2", "Adora2a", "Ppp1r1b", "Tac1", "Meis2", "Cxcl14", "Sln", "Nfix", "Otx2", "Moxd1", "Dlx1", "Lhx6", "Crh", "Zeb2", "Lhx8", "Hmx2", "Gal", "Nts", "Dlx1", "Ucn3", "Meis2", "Samd3", "Eomes", "Lhx9", "Nfix", "Thrsp"))) GeneLists[["Hypomap_DMH_LH_AH"]] =unique(toupper(c("Zfp804b", "Hmcn1", "Tcf7l2", "Vcan","Tac1", "Prdm13", "Tcf4", "Crh", "Lhx9", "Adgrf5", "Nxph4", "", "Alx1", "Pmfbp1", "Onecut2", "", "Npsr1", "Tmem114", "Lef1", "Fign", "Gal", "Crh", "Nts", "Piezo2", "Lhx6", "", "Crhbp", "Dlx1", "Ebf2", "Trh", "Lhx6", "Sox6", "Lhx8", "Syt2", "Trh", "Meis2", "Nts", "Ebf1", "Nkx2-4", "Otx1", "Gng8", "Samd3", "Tbr1", "Ndnf", "Meis2", "Samd3", "Ntng2", "Lef1", "Cped1", "Hcrt", "Rfx4", "Stk26", "Nxph4", "Pmch", "Pou6f2", "Slc6a3", "Onecut2", "Nkd1", "Cd24a", "Lhx1", "Gal", "Hmcn1", "Mc4r", "Fezf1"))) GeneLists[["Hypomap_Peri_ZI"]] =unique(toupper(c("Cd79a", "Fam122b", "Vipr2", "Sst", "Tent5a", "Sparc", "Npy", "Sst", "Tbx19", "Il1rapl2", "Col11a1", "Sox14", "Lef1", "Myo5b", "Npsr1", "Barhl2", "Lef1", "Pbx3", "Lhx6", "Pvalb", "Shisal2b", "Onecut3", "Onecut2", "Foxp2", "Tmem114", "Lef1", "Meis2"))) GeneLists[["Hypomap_SCN_VMH"]] =unique(toupper(c("Vip", "Vipr2", "Chodl", "Igfbp4", "Cck", "Nms", "Fam122b", "Prok2", "Prr5l", "Cdkn1c", "Lhx1", "Grp", "Cd24a", "Rrad", "Gpr149", "Fezf1", "Col24a1", "Gpr88", "Arhgap36", "Cenpf", "Egflam", "Sox14", "Nkx2-2", "Satb2", "Cd40", "Nts", "Gldn", "Cbln4", "Necab1", "Esr1", "Iigp1", "Smim3", "Rai14", "Hmcn2", "Gpr149", "Slit3", "Nr5a1", "Tcf7l2", "Hmcn2", "Lamp5", "Bcl11b", "Nkd2", "Nr5a1", "H2-Q2", "Tac1", "Fezf1", "Robo3", "Sparc", "Nr5a1", "C1ql2", "Nr5a2", "Nfia", "Tcf4", "Adcyap1", "Tac1", "Lef1")))
Gene Lists: Betul (Endo/Epeny/Tany)
GeneLists = list() GeneLists[["EpendyTanyList"]] = c("FOXJ1", "RAX", "ADM", "EMX2", "LHX2", "TMEM212", "CRYM", "CCDC153", "RFX2", "RFX3", "RFX4", "NPAS3", "COL23A1", "CD59", "SLC17A8", "VCAN", "FRZB", "PENK", "COL25A1", "CACNA2D2", "STOML3", "TMEM212", "SLC7A11", "PCP4L1", "PLTP", "TGFB2", "NR2E1", "EPHB1", "P3H2", "NELL2", "FGF10", "RGCC", "GRIA2", "RLBP1", "SIX6", "SCN7A", "MEST", "IGFBP5", "RGS7BP") GeneLists[["EpendyTany_CampbellHeatmap"]] = toupper(c("Stoml3", "Ccdc153", "Tmem212", "Pcp4l1", "Tm4sf1", "Pltp", "Slc1a2", "Tgfb2", "Agt", "Gfap", "Slc7a11", "Vcan", "Nr2e1", "Ephb1", "P3h2", "Nell2", "Gria2", "Rlbp1", "Frzb", "Penk", "Six6", "Fgf10", "Scn7a", "Fndc3c1", "Mest", "Col25a1", "Igfbp5", "Rgcc", "Adm", "Rgs7bp", "Ctgf")) GeneLists[["EndoList"]] = c("SLCO1C1", "SLC38A5", "MYH11", "MRC1", "CLDN5", "ITM2A", "FLT1") GeneLists[["VLMC"]] = c("COL1A1", "COL3A1", "LUM", # Campbell "PTGDS", # Zhou "DCN" ) ## Mural cells are the vascular smooth muscle cells (vSMCs), and pericytes, ## of the microcirculation. GeneLists[["Mural"]] = c("MUSTN1", "SLCO1C1", "CSPG4", # Campbell "PDGFRB", "NDUFA4L2", # Zhou_Fig1_MarkerGenes "MYL9" # Valandewijck ** Pan-mural cell marker ) GeneLists[["Pericytes"]] = c("KCNJ8", "PDGFRB", "CSPG4", "ANPEP", # Valandewijck_Fig1 "RGS5", "CD248", "ABCC9", "VTN", "S1PR3" # Valandewijck_Fig1 ) GeneLists[["vSMCs"]] = c("PDLIM3", "ACTA2", "TAGLN", "MYH11", # Valandewijck_Fig1 "MYL9", "MYLK", "SNCG", "CNN1", "PLN" # Valandewijck_Fig1 ) ## ECs: Endothelial Cells GeneLists[["Endothelium"]] = unique(c("CLDN5", # Zhou, Valandewijck_Fig1, Chen: Endo "FLT1", "SLC38A5", # Zhou_Fig1_MarkerGenes ** "SCL38A5"-Chen: Endo1 "SLCO1C1", # Campbell "MFSD2A", # Valandewijck ** Endothelial-specific transcript "MYH11", "MRC1", # Chen: Endo2 "ITM2A", "PECAM1", "CDH5", # Kalucka "BCAM", "ESAM", "LY6C1", "LY6A", # Kalucka_FigS1 "CD36", "SOX17", "NRP1", # Kalucka_FigS1 "PGLYRP1", # Kalucka "FOXQ1", "FOXF2", # Kalucka_Fig2 "PECAM1", "KDR", "FLT1", "TIE1", # Valandewijck_Fig1 "TEK", "ICAM2", "PODXL", "PTPRB" # Valandewijck_Fig1 )) GeneLists[["LargeArtery"]] = unique(c("MGP", "CYTL1", "FBLN5", "BMX", # Kalucka_FigS3 "CLU", "ELN", "BGN", "IGFBP4", # Kalucka_TableS2 "CFH", "LMCD1", "BPGM" # Kalucka_TableS2 )) GeneLists[["Artery"]] = unique(c("BMX", # Zeisel "GKN3", "HEY1", # Kalucka, Zeisel "EDN3", # Kalucka_TableS2, Zeisel_FigS3 "CLDN5", "ADGRF5", "VTN", "CSPG4", "ATP13A5", # Zeisel_Fig5 "PTH1R", "TAGLN", "IGFBP2", "DCN", "LUM", # Zeisel_Fig5 "PDGFRA", "IL33", # Zeisel_Fig5 "ATF3", "CRIP1", "FBLN2", "GJA4", "MECOM", # Kalucka_TableS5 "SAT1", "SEMA3G", "SOX17", # Kalucka_TableS5 "EGFL8", "VEGFC", "HRCT1", "CDKN1C", # Kalucka_TableS2 "EPS8L2", "ALPL", "MGST1", # Kalucka_TableS2 "NR4A2", "JUNB", "GATA6", "STAT4", "SETBP1", # Valandewijck_Ext6 "NFATC4", "ATF3", "MYBL2", "CSRNP1", "NR4A1", # Valandewijck_Ext6 "IRF6", "ZKSCAN4", "FOS", "GATA2", "MIER2", # Valandewijck_Ext6 "HSF4", "EGR1", "ZBP1", "BHLHE41", "TRF", # Valandewijck_Ext6 "SLC36A1", "MTTP", "SLC12A5", "SCNN1A", # Valandewijck_Ext6 "ATP2A3", "SLC48A1", "ABCA5", "GRIA1", # Valandewijck_Ext6 "SLC6A8", "ATP11A", "PLSCR2", "SLC12A8", # Valandewijck_Ext6 "SLC45A4", "GJA5", "KCNN4", "SNAP25", "FXYD4" # Valandewijck_Ext6 )) # Arteriolar ECs express both arterial and capillary markers # (capillary-arterial) GeneLists[["Arteriolar"]] = unique(c("GLUL", "SLC26A10", # Kalucka_FigS3 "TGFB2", # Kalucka_FigS5 "RAD54B", "TUBA1A", "STMN1", "AKAP12", # Kalucka_TableS2 "KLF6", "DUSP1", "ZFP771" # Kalucka_TableS2 )) GeneLists[["Capillary"]] = unique(c("MEOX1", # Zeisel "CLDN5", "ADGRF5", "EMCN", # Zeisel_Fig5 "APLN", "CD82", "CHST1", "APOD", # Zeisel_Fig5 "CXCL12", "SPOCK2", # Kalucka_FigS3 "MFSD2A", "RGCC", # Kalucka_FigS5 "ABLIM1", "BSG", "SLCO1A4", "SLCO1C1", # Kalucka_TableS5 "SPOCK2", "TSC22D1", # Kalucka_TableS5 "CD300LG", "SGK1", "SPARC", # Kalucka_TableS5 "GM9946", "LRRN3", "GSTM7", "PALM", # Kalucka_TableS2 "TBX3", "PRDM16", "RASGRP2", "CD83", # Kalucka_TableS2 "ANGPT2", # Valandewijck_Fig2 "PRDM11", "PLEK", "HEYL", "MEOX1", "ZFP382", # Valandewijck_Ext6 "ZFP457", "SALL1", "TBX2", "KLF15", "LBX2", # Valandewijck_Ext6 "GLI3", "HNF1A", "HIF3A", "TUB", "FOXM1", # Valandewijck_Ext6 "ZBTB41", "ETV5", "PRDM1", "ZFHX2", "ZFP334" # Valandewijck_Ext6 )) # Venular ECs express both venous and capillary markers # (capillary-venous) GeneLists[["Venular"]] = unique(c("CAR4", "ITM2A", # Kalucka_FigS3 "TFRC", "CAR4", # Kalucka_FigS5 "GATM", "HMCN1", "SLC7A1", "NKD1", # Kalucka_TableS2 "HSPA1A", "SMOX", "SLC31A1", "SLC39A8" # Kalucka_TableS2 )) GeneLists[["Venous"]] = unique(c("SLC38A5", # Zeisel "CLDN5", "ADGRF5", "EMCN", # Zeisel_Fig5 "APOD", "CTSC", # Kalucka_TableS5 "IL6ST", "APOE", "CTLA2A", # Kalucka_TableS5 "NR2F2", # Valandewijck_Fig2 "ZC3H12B", "THAP6", "TCF15", "KLF10", "AR", # Valandewijck_Ext6 "CARHSP1", "ZFP691", "ZFAT", "SOX12", "BCL11A", # Valandewijck_Ext6 "ZFP341", "DACH2", "MBNL3", "CREB3L4", "TBX1", # Valandewijck_Ext6 "LCN2", "CACNA1B", "PTGDS", "KCNB1" # Valandewijck_Ext6 )) GeneLists[["LargeVein"]] = c("TMSB10", "ICAM1", "VWF", "ACKR1", # Kalucka_FigS3 "SLC38A5", "LCN2", # Kalucka_FigS5 "VCAM1", "TMEM252", "CTSC", "TGM2", "ACKR1" # Kalucka_TableS2 ) GeneLists[["Choroid_ECs"]] = c("PLVAP", "KDR", "PLPP3", # Kalucka_FigS3 "PLPP1", "CD24A", "NRP1", "ESM1", # Kalucka "SOCS3", "RGCC", "IGFBP3", "EHD4" # Kalucka_TableS2 ) # Interferon-activated ECs GeneLists[["Interferon_ECs"]] = c("ISG15", "IFIT1", "IFIT3", "IFIT3B", # Kalucka_FigS3 "PGLYRP1", "SLC7A5", "SPARCL1", # Kalucka_TableS2 "SLC3A2", "SLC38A3", "USP18" # Kalucka_TableS2 ) # Glia GeneLists[["EpendymalCells"]] = unique(c("SOX2", # Campbell "VIM", "NES", # Langlet, Chen ** NES&VIM (radial glia markers) are highly expressed in tany&ependy "PRDX6", "MT1", "MT2", "DLK1" # Campbell_S2d )) GeneLists[["Ependy"]] = unique(c("CCDC153", # Campbell_Fig1, Chen, Zhou_Fig1_MarkerGenes "CCDC74A", "CCDC74B", "LRTOMT", # Zhou_MarkerGenes "FOXJ1", "RFX2", "RFX3", "RFX4", "STOML3", "TMEM212", "PCP4L1", "TM4SF1", "PLTP",# Campbell_Fig2 "CDHR4", "CALB1", "ITIH5", # Campbell_S2d "HDC" # Chen_Fig4 )) GeneLists[["EpyII"]] = c("FLT1", "RSPO3", "RLBP1", "LRRN1" # Campbell_S2d ) # Tanycytes: blood-brain traffic controllers, metabolic modulators, neural stem/progenitor cells GeneLists[["Tanycytes"]] = c("RAX", "ADM", "CRYM", # Campbell_Fig1 "FRZB", "COL23A1", "SLC16A2", "LHX2", "PTN", # Langlet, Chen ** SLC16A2 & LHX2 have been linked to tanycyte development "PPP1R1B", "DIO2", "PRDX6", # Langlet "GCK", "DIO3", "OATP1C1", "GPR50", "NMUR2", # Langlet_Table1 "ALDH1A1", "TTR", "CRBP1", "STRA6", # Langlet_Table1 "CRABP2", "CNTF", # Langlet_Table1 "FGF10" # Steuernagel ) # Alpha Tanycytes: gene markers are more similar to non-tanycyte ependymal cells than beta tanycytes # modulation of neuronal activity GeneLists[["ALPHA"]] = c("SLC1A3", "SLC16A1", "SLC16A4", # Langlet ** Glutamate and lactate transport - neuronal modulation "GJA1", "FGF18", "PRSS56", "CD59A", "CRYM", # Langlet "VCAN" ) GeneLists[["Alpha1"]] = c("SLC1A2", "TGFB2", "AGT", "GFAP", # Campbell_Fig2 "SLC7A11", # Campbell_Fig2 "RSPO3", # Campbell_S2d "SLC17A8", # Chen_Fig4 "MAFB", # Yoo_Fig3 "OCLN" # Langlet_Table1 ) GeneLists[["alpha1.1"]] = c("RSPO3", "TMEM212", "ITIH5", "FLT1" # Campbell_S2d ) GeneLists[["alpha1.2"]] = c("RSPO3", "SLC17A8", "LYZ2" # Campbell_S2d ) GeneLists[["Alpha2"]] = c("VCAN", "NR2E1", "EPHB1", "P3H2", "NELL2", # Campbell_Fig2 "PDZPH1", # Campbell_S2d "FRZB", "PENK" # Langlet ) # Beta Tanycytes: component of blood-brain interface # control the access of nutrients and hormones to brain & secretion of # neuropeptides into hypophysial vascular system in the ME GeneLists[["BETA"]] = c("CLDN1", "VEGF", "CDH2", "CAVL", "SLC2A1", # Langlet ** SLC2A1 is expressed more in beta1 "SOX2", "FGF10", "BLBP", "MSI1", # Langlet ** Neural stem cell markers - Stem/Progenitor cell fxn "FGFR1", "CNTFR", # Langlet ** Growth factor rec. - Stem/Progenitor cell fxn. "COL25A1", "CACNA2D2", "ADM", # Langlet "CLDN10" # Campbell_S2d ) GeneLists[["Beta1"]] = unique(c("GRIA2", "RLBP1", "FRZB", "PENK", # Campbell_Fig2 "SIX6", "FGF10", # Campbell_Fig2 "LRRN1", # Campbell_S2d "FRZB", "PENK" # Langlet )) GeneLists[["Beta2"]] = unique(c("SCN7A", "FNDC3C1", "MEST", "ADM", # Campbell_Fig2 "COL25A1", "IGFBP5", "RGCC", "RGS7BP", # Campbell_Fig2 "CTGF", # Campbell_Fig2 "TRHDE" # Langlet_Table1 )) GeneLists[["beta2.1"]] = c("FRZB", "SIM1" # Campbell_S2d ) GeneLists[["beta2.2"]] = c("CYSLTR1", "LRRTM3" # Campbell_S2d ) for(GL in names(GeneLists)){ GeneLists[[GL]] = subset(GeneLists[[GL]], GeneLists[[GL]] %in% row.names(KaZhouAll)) }
Read in ALL data and iterate UMAPs
KaZhouAll = readRDS("~/Dropbox/LabMac/KaZhouAll_mt10_integrated.rds") CleanedClusters_Figure1 = read.csv("~/Library/CloudStorage/Box-Box/HG2553 Main Folder/Science Advances/CleanedClusters_Figure1_19DEC22.csv", row.names =1) KaZhouAll@meta.data$Timepoint = gsub("_.*", "", gsub("T", "", gsub("CS13", "GW6", gsub("CS14", "GW7", gsub("CS15", "GW7", gsub("CS22", "GW10", KaZhouAll@meta.data$sample)))))) KaZhouAll@meta.data$Timepoint2 = ifelse(KaZhouAll@meta.data$Study == "Zhou", paste(KaZhouAll@meta.data$Timepoint, "[Zhou]"), KaZhouAll@meta.data$Timepoint) MainClusters = CleanedClusters_Figure1 %>% dplyr::select("Pop") row.names(MainClusters) = CleanedClusters_Figure1$Row.names MainClusters$Pop = gsub(" \\[.*", "", MainClusters$Pop) KaZhouAll = AddMetaData(KaZhouAll, MainClusters, "MainClusters") Idents(KaZhouAll) = "MainClusters" GenesToPlot = GeneLists[["OligList"]] #GeneLists[["AstroMicro"]] #c(GeneLists[["Zhou_MainMarkers"]], GeneLists[["Zhou_MainMarkersV2"]]) #"Neuroepithelial" , "Ependymal", "RadialGlia", "Dividing", "Endothelial", "Tanycytes", "Microglia", "VLMC", "IntProgen", for(Pops in c("Oligodendrocytes")){ SubsetSeu = subset(KaZhouAll, idents = Pops) DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = FindVariableFeatures(SubsetSeu) SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) #VF_new = SubsetSeu@assays$integrated@var.features DPlist2 = list() DPlist = list() FPlist = list() Filename = paste(Pops, "29MAR23", sep="") for(y in seq(10,50,10)){ DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = RunPCA(SubsetSeu, npcs = y) for(z in seq(10,y,10)){ for(s in c(2,5,10)){ SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s) DefaultAssay(SubsetSeu) = "RNA" FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, GenesToPlot, reduction="umap") DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "Timepoint2") + labs(title = paste("PCA", y, "_dims", z, "_spread", s)) DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "Timepoint2", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s)) }}} pdf(paste("FETAL_HYPO_155K_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=77/1.3) print(FPlist) dev.off() pdf(paste("FETAL_HYPO_155K_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5) print(DPlist) dev.off() pdf(paste("FETAL_HYPO_155K_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5) print(DPlist2) dev.off() } for(Pops in c("Oligodendrocytes")){ SubsetSeu = subset(KaZhouAll, idents = Pops) DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = FindVariableFeatures(SubsetSeu) SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DPlist2 = list() DPlist = list() FPlist = list() Filename = paste(Pops, "29MAR23_Harmony", sep="") for(y in seq(10,50,10)){ DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = RunPCA(SubsetSeu, npcs = y) SubsetSeu <- RunHarmony(SubsetSeu, group.by.vars = "Timepoint2") for(z in seq(10,y,10)){ for(s in c(2,5,10)){ SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s, reduction = "harmony") DefaultAssay(SubsetSeu) = "RNA" FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, GenesToPlot, reduction="umap") DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "Timepoint2") + labs(title = paste("PCA", y, "_dims", z, "_spread", s)) DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "Timepoint2", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s)) }}} pdf(paste("FETAL_HYPO_155K_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=77/1.3) print(FPlist) dev.off() pdf(paste("FETAL_HYPO_155K_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5) print(DPlist) dev.off() pdf(paste("FETAL_HYPO_155K_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5) print(DPlist2) dev.off() }
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Non Neuronal Subclustering
Microglia
GeneLists[["Olah_Microglia"]] = c("PTPRC", "ITGAM", "AIF1", "C1QA", "CTSS", "CD14", "CSF3R", "ARGLU1", "FAM46A", "IFIT3", "ISG15", "MRC1", "TNF", "CD83", "EGR2", "TNFSF18", "CCL8", "TFRC", "FCGBP", "GPR84", "PONA", "CDC20", "BIRC5") #Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease GeneLists[["Tansley_Microglia"]] = c("TMEM119", "FCRTS", "P2RY12", "CX3CR1", "TREM2", "C1QA", "GM3336", "SPP1", "IFIT3", 'MKI67', "MCM6", "CLDN5", "H2-EB1") #Single-cell RNA sequencing reveals time- and sex-specific responses of mouse spinal cord microglia to peripheral nerve injury and links ApoE to chronic pain Idents(KaZhouAll) = "MainClusters" Microglia_Seu = subset(KaZhouAll, idents = "Microglia") DefaultAssay(Microglia_Seu) = "integrated" Microglia_Seu = FindVariableFeatures(Microglia_Seu) Microglia_Seu = ScaleData(Microglia_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Microglia_Seu) = "integrated" Microglia_Seu = RunPCA(Microglia_Seu, npcs = 20) Microglia_Seu <- RunUMAP(Microglia_Seu, dims = 1:20, spread= 2) DefaultAssay(Microglia_Seu) = "RNA" ##### set.dim = c(20) set.res = 1 set.kparam = c(30) #ClusterFunc_All_RNA(Microglia_Seu) DefaultAssay(Microglia_Seu) = "integrated" Microglia_Seu <- FindNeighbors(Microglia_Seu, k.param=30, dims=1:20) Microglia_Seu <- FindClusters(Microglia_Seu, resolution = 1) DefaultAssay(Microglia_Seu) = "RNA" #CheckInput = Microglia12Clean #CheckUMAP(Microglia_Seu) Microglia_UMAP = as.data.frame(Microglia_Seu@reductions$umap@cell.embeddings) Microglia01 = subset(Microglia_Seu, idents = c(0)) Microglia01Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia01) & Microglia_UMAP$UMAP_1 > -2 & Microglia_UMAP$UMAP_1 < 8 & Microglia_UMAP$UMAP_2 > 0) Microglia02 = subset(Microglia_Seu, idents = 1) Microglia02Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia02) & Microglia_UMAP$UMAP_1 < 2 & Microglia_UMAP$UMAP_2 > 0.5) Microglia03 = subset(Microglia_Seu, idents = c(2,5)) Microglia03Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia03) & Microglia_UMAP$UMAP_1 > 2 & Microglia_UMAP$UMAP_1 < 7 & Microglia_UMAP$UMAP_2 > -5 & Microglia_UMAP$UMAP_2 < 4.5 | Microglia_UMAP$UMAP_1 > -5 & Microglia_UMAP$UMAP_1 < 2 & Microglia_UMAP$UMAP_2 > -2.5 & Microglia_UMAP$UMAP_2 < 1.5) Microglia04 = subset(Microglia_Seu, idents = 3) Microglia04Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia04)& Microglia_UMAP$UMAP_1 >3) Microglia05 = subset(Microglia_Seu, idents = c(4,8)) Microglia05Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia05) & Microglia_UMAP$UMAP_1 > -5 & Microglia_UMAP$UMAP_2 < -10) Microglia06 = subset(Microglia_Seu, idents = 6) Microglia06Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia06) & Microglia_UMAP$UMAP_1 > -4 & Microglia_UMAP$UMAP_1 < 2 & Microglia_UMAP$UMAP_2 > -10) Microglia07Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia06) & Microglia_UMAP$UMAP_1 > 8) Microglia08 = subset(Microglia_Seu, idents = c(7)) Microglia08Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia08) & Microglia_UMAP$UMAP_1 < -10) Microglia09 = subset(Microglia_Seu, idents = c(9, 10)) Microglia09Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia09) & Microglia_UMAP$UMAP_1 > -9.2 & Microglia_UMAP$UMAP_1 < -4 & Microglia_UMAP$UMAP_2 < -12) Microglia10Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia09) & ! row.names(Microglia_UMAP) %in% row.names(Microglia09Clean) & Microglia_UMAP$UMAP_1 > -11 & Microglia_UMAP$UMAP_2 < -8) Microglia11 = subset(Microglia_Seu, idents = 11) Microglia11Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia11) & Microglia_UMAP$UMAP_1 > -10 & Microglia_UMAP$UMAP_1 < 0) Microglia12 = subset(Microglia_Seu, idents = 12) Microglia12Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia12) & Microglia_UMAP$UMAP_1 < -7) Microglia13 = subset(Microglia_Seu, idents = 13) Microglia13Clean = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia13) & Microglia_UMAP$UMAP_1 < -8 & Microglia_UMAP$UMAP_2 > -10) #CheckInput = Microglia03_Extras #CheckUMAP(Microglia_Seu) Microglia_REST = subset(Microglia_Seu, cells = c(row.names(Microglia01Clean), row.names(Microglia02Clean), row.names(Microglia03Clean), row.names(Microglia04Clean), row.names(Microglia05Clean), row.names(Microglia06Clean), row.names(Microglia07Clean), row.names(Microglia08Clean), row.names(Microglia09Clean), row.names(Microglia10Clean), row.names(Microglia11Clean), row.names(Microglia12Clean), row.names(Microglia13Clean)), invert=T) #CheckInput = Microglia05_Extras #CheckUMAP(Microglia_Seu) Microglia01_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 1 & Microglia_UMAP$UMAP_2 > 3 | row.names(Microglia_UMAP) %in% row.names(Microglia01Clean)) Microglia02_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 < 1 & Microglia_UMAP$UMAP_1 > -6.2 & Microglia_UMAP$UMAP_2 > 0 | row.names(Microglia_UMAP) %in% row.names(Microglia02Clean)) Microglia03_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & ! row.names(Microglia_UMAP) %in% c(row.names(Microglia02_Extras), row.names(Microglia01_Extras)) & Microglia_UMAP$UMAP_1 > -4 & Microglia_UMAP$UMAP_1 < 8 & Microglia_UMAP$UMAP_2 < 4 & Microglia_UMAP$UMAP_2 > -3.2 | row.names(Microglia_UMAP) %in% row.names(Microglia03Clean)) Microglia03_Extras = subset(Microglia03_Extras, ! row.names(Microglia03_Extras) %in% c(row.names(Microglia02_Extras), row.names(Microglia01_Extras))) Microglia04_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 3 & Microglia_UMAP$UMAP_2 < -2 & Microglia_UMAP$UMAP_2 > -13 & ! row.names(Microglia_UMAP) %in% row.names(Microglia03_Extras) | row.names(Microglia_UMAP) %in% row.names(Microglia04Clean)) Microglia05_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -5 & Microglia_UMAP$UMAP_1 < 3 & Microglia_UMAP$UMAP_2 < -9 | row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 4 & Microglia_UMAP$UMAP_2 < -12 | row.names(Microglia_UMAP) %in% row.names(Microglia05Clean)) Microglia06_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -4 & Microglia_UMAP$UMAP_1 < 3 & Microglia_UMAP$UMAP_2 > -10 & Microglia_UMAP$UMAP_2 < -3.2 | row.names(Microglia_UMAP) %in% row.names(Microglia06Clean)) Microglia07_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > 8 & Microglia_UMAP$UMAP_2 < 2 | row.names(Microglia_UMAP) %in% row.names(Microglia07Clean)) Microglia08_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 < -10 & Microglia_UMAP$UMAP_2 > -1 | row.names(Microglia_UMAP) %in% row.names(Microglia08Clean)) Microglia09_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -9.2 & Microglia_UMAP$UMAP_1 < -4 & Microglia_UMAP$UMAP_2 < -12 | row.names(Microglia_UMAP) %in% row.names(Microglia09Clean)) Microglia10_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -11 & Microglia_UMAP$UMAP_1 < -5 & Microglia_UMAP$UMAP_2 < -8 & ! row.names(Microglia_UMAP) %in% row.names(Microglia09_Extras)| row.names(Microglia_UMAP) %in% row.names(Microglia10Clean)) Microglia11_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_2 > -8 & Microglia_UMAP$UMAP_2 < -2 & Microglia_UMAP$UMAP_1 > -8 & Microglia_UMAP$UMAP_1 < -3 & ! row.names(Microglia_UMAP) %in% row.names(Microglia06_Extras)| row.names(Microglia_UMAP) %in% row.names(Microglia11Clean)) Microglia12_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 > -10 & Microglia_UMAP$UMAP_1 < -6 & Microglia_UMAP$UMAP_2 > -2 & Microglia_UMAP$UMAP_2 < 1 | row.names(Microglia_UMAP) %in% row.names(Microglia12Clean)) Microglia13_Extras = subset(Microglia_UMAP, row.names(Microglia_UMAP) %in% colnames(Microglia_REST) & Microglia_UMAP$UMAP_1 < -8 & Microglia_UMAP$UMAP_2 > -10 & Microglia_UMAP$UMAP_2 < -5 | row.names(Microglia_UMAP) %in% row.names(Microglia13Clean)) Microglia_Assigns = GenerateMetaData_Barcodes(list("Microglia_01" = Microglia01_Extras, "Microglia_02" = Microglia02_Extras, "Microglia_03" = Microglia03_Extras, "Microglia_04" = Microglia04_Extras, "Microglia_05" = Microglia05_Extras, "Microglia_06" = Microglia06_Extras, "Microglia_07" = Microglia07_Extras, "Microglia_08" = Microglia08_Extras, "Microglia_09" = Microglia09_Extras, "Microglia_10" = Microglia10_Extras, "Microglia_11" = Microglia11_Extras, "Microglia_12" = Microglia12_Extras, "Microglia_13" = Microglia13_Extras)) #Microglia02_Extras_Pt2 = subset(Microglia_Seu, cells = Microglia_Assigns$Barcodes, invert=T) #CheckInput = Microglia03_Extras #CheckUMAP(Microglia_Seu) Microglia_Assigns$Dups = duplicated(Microglia_Assigns$Barcodes) | duplicated(Microglia_Assigns$Barcodes, fromLast=T) Microglia_Assigns_T = subset(Microglia_Assigns, Microglia_Assigns$Dups == T) unique(Microglia_Assigns_T$Pop) Microglia_Assigns = GenerateMetaData(list( "Microglia_01" = Microglia01_Extras, "Microglia_02" = Microglia02_Extras, "Microglia_03" = Microglia03_Extras, "Microglia_04" = Microglia04_Extras, "Microglia_05" = Microglia05_Extras, "Microglia_06" = Microglia06_Extras, "Microglia_07" = Microglia07_Extras, "Microglia_08" = Microglia08_Extras, "Microglia_09" = Microglia09_Extras, "Microglia_10" = Microglia10_Extras, "Microglia_11" = Microglia11_Extras, "Microglia_12" = Microglia12_Extras, "Microglia_13" = Microglia13_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
VLMC
Idents(KaZhouAll) = "MainClusters" VLMC_Seu = subset(KaZhouAll, idents = "VLMC") DefaultAssay(VLMC_Seu) = "integrated" VLMC_Seu = FindVariableFeatures(VLMC_Seu) VLMC_Seu = ScaleData(VLMC_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(VLMC_Seu) = "integrated" VLMC_Seu = RunPCA(VLMC_Seu, npcs = 30) VLMC_Seu <- RunHarmony(VLMC_Seu, group.by.vars = "Timepoint2") VLMC_Seu <- RunUMAP(VLMC_Seu, dims = 1:30, spread= 5, reduction = "harmony") DefaultAssay(VLMC_Seu) = "RNA" ##### set.dim = c(30) set.res = 1 set.kparam = c(30, 50, 100) #ClusterFunc_All_RNA(VLMC_Seu) DefaultAssay(VLMC_Seu) = "integrated" VLMC_Seu <- FindNeighbors(VLMC_Seu, k.param=30, dims=1:30) VLMC_Seu <- FindClusters(VLMC_Seu, resolution = 1) DefaultAssay(VLMC_Seu) = "RNA" #CheckInput = VLMC08Clean #CheckUMAP(VLMC_Seu) VLMC_UMAP = as.data.frame(VLMC_Seu@reductions$umap@cell.embeddings) VLMC01 = subset(VLMC_Seu, idents = c(0)) VLMC01Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC01) & VLMC_UMAP$UMAP_1 < -23 & VLMC_UMAP$UMAP_2 > -15) VLMC02 = subset(VLMC_Seu, idents = 4) VLMC02Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC02) & VLMC_UMAP$UMAP_1 > -26 & VLMC_UMAP$UMAP_1 < -14 & VLMC_UMAP$UMAP_2 > -8) VLMC03 = subset(VLMC_Seu, idents = c(9)) VLMC03Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC03) & VLMC_UMAP$UMAP_1 < -15) VLMC04 = subset(VLMC_Seu, idents = 3) VLMC04Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC04) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 0) VLMC05 = subset(VLMC_Seu, idents = c(7)) VLMC05Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC05) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 > -25 & VLMC_UMAP$UMAP_2 < -5) VLMC06 = subset(VLMC_Seu, idents = 11) VLMC06Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC06) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 < -25) VLMC07 = subset(VLMC_Seu, idents = c(7,11,3,9,4,0), invert=T) VLMC07Clean = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC07) & VLMC_UMAP$UMAP_1 > 0 & VLMC_UMAP$UMAP_2 > -10 | row.names(VLMC_UMAP) %in% colnames(VLMC07) & VLMC_UMAP$UMAP_1 > 8 ) #CheckInput = VLMC03_Extras #CheckUMAP(VLMC_Seu) VLMC_REST = subset(VLMC_Seu, cells = c(row.names(VLMC01Clean), row.names(VLMC02Clean), row.names(VLMC03Clean), row.names(VLMC04Clean), row.names(VLMC05Clean), row.names(VLMC06Clean), row.names(VLMC07Clean)), invert=T) #CheckInput = VLMC07_Extras #CheckUMAP(VLMC_Seu) VLMC01_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 < -25 & VLMC_UMAP$UMAP_2 > -11 | row.names(VLMC_UMAP) %in% row.names(VLMC01Clean)) VLMC02_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -25 & VLMC_UMAP$UMAP_1 < -14 & VLMC_UMAP$UMAP_2 > -11 | row.names(VLMC_UMAP) %in% row.names(VLMC02Clean)) VLMC03_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & ! row.names(VLMC_UMAP) %in% c(row.names(VLMC02_Extras), row.names(VLMC01_Extras)) & VLMC_UMAP$UMAP_1 < -14 | row.names(VLMC_UMAP) %in% row.names(VLMC03Clean)) VLMC04_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 0 & VLMC_UMAP$UMAP_2 > 0 | row.names(VLMC_UMAP) %in% row.names(VLMC04Clean)) VLMC05_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 > -25 & VLMC_UMAP$UMAP_2 < -5 | row.names(VLMC_UMAP) %in% row.names(VLMC05Clean)) VLMC06_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > -14 & VLMC_UMAP$UMAP_1 < 10 & VLMC_UMAP$UMAP_2 < -25| row.names(VLMC_UMAP) %in% row.names(VLMC06Clean)) VLMC07_Extras = subset(VLMC_UMAP, row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > 0 & VLMC_UMAP$UMAP_2 > -10 | row.names(VLMC_UMAP) %in% colnames(VLMC_REST) & VLMC_UMAP$UMAP_1 > 8 | row.names(VLMC_UMAP) %in% row.names(VLMC07Clean)) VLMC_Assigns = GenerateMetaData_Barcodes(list("VLMC_01" = VLMC01_Extras, "VLMC_02" = VLMC02_Extras, "VLMC_03" = VLMC03_Extras, "VLMC_04" = VLMC04_Extras, "VLMC_05" = VLMC05_Extras, "VLMC_06" = VLMC06_Extras, "VLMC_07" = VLMC07_Extras)) #VLMC02_Extras_Pt2 = subset(VLMC_Seu, cells = VLMC_Assigns$Barcodes, invert=T) #CheckInput = VLMC03_Extras #CheckUMAP(VLMC_Seu) VLMC_Assigns$Dups = duplicated(VLMC_Assigns$Barcodes) | duplicated(VLMC_Assigns$Barcodes, fromLast=T) VLMC_Assigns_T = subset(VLMC_Assigns, VLMC_Assigns$Dups == T) unique(VLMC_Assigns_T$Pop) VLMC_Assigns = GenerateMetaData(list( "VLMC_01" = VLMC01_Extras, "VLMC_02" = VLMC02_Extras, "VLMC_03" = VLMC03_Extras, "VLMC_04" = VLMC04_Extras, "VLMC_05" = VLMC05_Extras, "VLMC_06" = VLMC06_Extras, "VLMC_07" = VLMC07_Extras)) #load("~/Hypothalamus_Subclustering_APR2023.RData") save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Pericytes
GeneLists[["Garcia_Pericytes"]] = c("ANXA2", "PDGFRB", "ANO3", "RERGL", "STAC", "APPBP2", "TERF2IP", "PCSK7", "GABRE", "KLHL29", "PGAP1") #Single-cell dissection of the human brain vasculature Idents(KaZhouAll) = "MainClusters" Pericytes_Seu = subset(KaZhouAll, idents = "Pericytes") DefaultAssay(Pericytes_Seu) = "integrated" Pericytes_Seu = FindVariableFeatures(Pericytes_Seu) Pericytes_Seu = ScaleData(Pericytes_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Pericytes_Seu) = "integrated" Pericytes_Seu = RunPCA(Pericytes_Seu, npcs = 20) Pericytes_Seu <- RunHarmony(Pericytes_Seu, group.by.vars = "Timepoint2") Pericytes_Seu <- RunUMAP(Pericytes_Seu, dims = 1:20, spread= 2, reduction = "harmony") DefaultAssay(Pericytes_Seu) = "RNA" ##### set.dim = c(20) set.res = 1 set.kparam = c(80) #ClusterFunc_All_RNA(Pericytes_Seu) DefaultAssay(Pericytes_Seu) = "integrated" Pericytes_Seu <- FindNeighbors(Pericytes_Seu, k.param=80, dims=1:20) Pericytes_Seu <- FindClusters(Pericytes_Seu, resolution = 1) DefaultAssay(Pericytes_Seu) = "RNA" #CheckInput = Pericytes03Clean #CheckUMAP(Pericytes_Seu) Pericytes_UMAP = as.data.frame(Pericytes_Seu@reductions$umap@cell.embeddings) Pericytes01 = subset(Pericytes_Seu, idents = c(0,1,2,3)) Pericytes01Clean = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes01) & Pericytes_UMAP$UMAP_1 < -2 & Pericytes_UMAP$UMAP_2 < 6 | row.names(Pericytes_UMAP) %in% colnames(Pericytes01) & Pericytes_UMAP$UMAP_1 > -2 & Pericytes_UMAP$UMAP_2 < 10) Pericytes02 = subset(Pericytes_Seu, idents = 4) Pericytes02Clean = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes02) & Pericytes_UMAP$UMAP_1 < 0 & Pericytes_UMAP$UMAP_2 > 6) Pericytes03 = subset(Pericytes_Seu, idents = c(5)) Pericytes03Clean = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes03) & Pericytes_UMAP$UMAP_1 > 0 & Pericytes_UMAP$UMAP_2 > 10) #CheckInput = Pericytes01_Extras #CheckUMAP(Pericytes_Seu) Pericytes_REST = subset(Pericytes_Seu, cells = c(row.names(Pericytes01Clean), row.names(Pericytes02Clean), row.names(Pericytes03Clean)), invert=T) Pericytes01_Extras = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 < -2 & Pericytes_UMAP$UMAP_2 < 6 | row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 > -2 & Pericytes_UMAP$UMAP_2 < 10 | row.names(Pericytes_UMAP) %in% row.names(Pericytes01Clean)) Pericytes02_Extras = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 < 0 & Pericytes_UMAP$UMAP_2 > 6 | row.names(Pericytes_UMAP) %in% row.names(Pericytes02Clean)) Pericytes03_Extras = subset(Pericytes_UMAP, row.names(Pericytes_UMAP) %in% colnames(Pericytes_REST) & Pericytes_UMAP$UMAP_1 > 0 & Pericytes_UMAP$UMAP_2 > 10 | row.names(Pericytes_UMAP) %in% row.names(Pericytes03Clean)) Pericytes_Assigns = GenerateMetaData_Barcodes(list("Pericytes_01" = Pericytes01_Extras, "Pericytes_02" = Pericytes02_Extras, "Pericytes_03" = Pericytes03_Extras)) #Pericytes02_Extras_Pt2 = subset(Pericytes_Seu, cells = Pericytes_Assigns$Barcodes, invert=T) #CheckInput = Pericytes03_Extras #CheckUMAP(Pericytes_Seu) Pericytes_Assigns$Dups = duplicated(Pericytes_Assigns$Barcodes) | duplicated(Pericytes_Assigns$Barcodes, fromLast=T) Pericytes_Assigns_T = subset(Pericytes_Assigns, Pericytes_Assigns$Dups == T) unique(Pericytes_Assigns_T$Pop) Pericytes_Assigns = GenerateMetaData(list( "Pericytes_01" = Pericytes01_Extras, "Pericytes_02" = Pericytes02_Extras, "Pericytes_03" = Pericytes03_Extras)) #load("~/Hypothalamus_Subclustering_APR2023.RData") save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
SMC
Idents(KaZhouAll) = "MainClusters" SMC_Seu = subset(KaZhouAll, idents = "vSMC") DefaultAssay(SMC_Seu) = "integrated" SMC_Seu = FindVariableFeatures(SMC_Seu) SMC_Seu = ScaleData(SMC_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(SMC_Seu) = "integrated" SMC_Seu = RunPCA(SMC_Seu, npcs = 20) SMC_Seu <- RunHarmony(SMC_Seu, group.by.vars = "Timepoint2") SMC_Seu <- RunUMAP(SMC_Seu, dims = 1:20, spread= 5, reduction = "harmony") DefaultAssay(SMC_Seu) = "RNA" ##### set.dim = c(20) set.res = 1 set.kparam = c(10) #ClusterFunc_All_RNA(SMC_Seu) DefaultAssay(SMC_Seu) = "integrated" SMC_Seu <- FindNeighbors(SMC_Seu, k.param=10, dims=1:20) SMC_Seu <- FindClusters(SMC_Seu, resolution = 1) DefaultAssay(SMC_Seu) = "RNA" #CheckInput = SMC02Clean #CheckUMAP(SMC_Seu) SMC_UMAP = as.data.frame(SMC_Seu@reductions$umap@cell.embeddings) SMC01 = subset(SMC_Seu, idents = c(0,3)) SMC01Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC01) & SMC_UMAP$UMAP_1 > 5) SMC02 = subset(SMC_Seu, idents = 1) SMC02Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC02) & SMC_UMAP$UMAP_1 < 0 & SMC_UMAP$UMAP_2 > 5) SMC03 = subset(SMC_Seu, idents = c(2)) SMC03Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC03) & SMC_UMAP$UMAP_1 < -10 & SMC_UMAP$UMAP_2 < 5) SMC04 = subset(SMC_Seu, idents = 4) SMC04Clean = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC04) & SMC_UMAP$UMAP_1 > -10 & SMC_UMAP$UMAP_1 < 5 & SMC_UMAP$UMAP_2 < 5) SMC_REST = subset(SMC_Seu, cells = c(row.names(SMC01Clean), row.names(SMC02Clean), row.names(SMC03Clean), row.names(SMC04Clean)), invert=T) #CheckInput = SMC02Clean #CheckUMAP(SMC_Seu) SMC01_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 > 5 | row.names(SMC_UMAP) %in% row.names(SMC01Clean)) SMC02_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 < 0 & SMC_UMAP$UMAP_2 > 5 | row.names(SMC_UMAP) %in% row.names(SMC02Clean)) SMC03_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 < -10 & SMC_UMAP$UMAP_2 < 5 | row.names(SMC_UMAP) %in% row.names(SMC03Clean)) SMC04_Extras = subset(SMC_UMAP, row.names(SMC_UMAP) %in% colnames(SMC_REST) & SMC_UMAP$UMAP_1 > -10 & SMC_UMAP$UMAP_1 < 5 & SMC_UMAP$UMAP_2 < 5 | row.names(SMC_UMAP) %in% row.names(SMC04Clean)) SMC_Assigns = GenerateMetaData_Barcodes(list("SMC_01" = SMC01_Extras, "SMC_02" = SMC02_Extras, "SMC_03" = SMC03_Extras, "SMC_04" = SMC04_Extras)) #SMC02_Extras_Pt2 = subset(SMC_Seu, cells = SMC_Assigns$Barcodes, invert=T) #CheckInput = SMC03_Extras #CheckUMAP(SMC_Seu) SMC_Assigns$Dups = duplicated(SMC_Assigns$Barcodes) | duplicated(SMC_Assigns$Barcodes, fromLast=T) SMC_Assigns_T = subset(SMC_Assigns, SMC_Assigns$Dups == T) unique(SMC_Assigns_T$Pop) SMC_Assigns = GenerateMetaData(list( "SMC_01" = SMC01_Extras, "SMC_02" = SMC02_Extras, "SMC_03" = SMC03_Extras, "SMC_04" = SMC04_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Radial Glia
Idents(KaZhouAll) = "MainClusters" RG_Seu = subset(KaZhouAll, idents = "RadialGlia") DefaultAssay(RG_Seu) = "integrated" RG_Seu = FindVariableFeatures(RG_Seu) RG_Seu = ScaleData(RG_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(RG_Seu) = "integrated" RG_Seu = RunPCA(RG_Seu, npcs = 20) RG_Seu <- RunHarmony(RG_Seu, group.by.vars = "Timepoint2") RG_Seu <- RunUMAP(RG_Seu, dims = 1:20, spread= 2, reduction = "harmony") DefaultAssay(RG_Seu) = "RNA" ##### set.dim = c(20) set.res = c(2,3) set.kparam = c(100) #ClusterFunc_All_RNA(RG_Seu) DefaultAssay(RG_Seu) = "integrated" RG_Seu <- FindNeighbors(RG_Seu, k.param=100, dims=1:20) RG_Seu <- FindClusters(RG_Seu, resolution = 3) DefaultAssay(RG_Seu) = "RNA" #CheckInput = RG09Clean #CheckUMAP(RG_Seu) RG_UMAP = as.data.frame(RG_Seu@reductions$umap@cell.embeddings) RG01 = subset(RG_Seu, idents = c(0,5)) RG01Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG01) & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > -6 & RG_UMAP$UMAP_2 < -4) RG15Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG01) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 0 & RG_UMAP$UMAP_2 < -1 & RG_UMAP$UMAP_2 > -8) RG02 = subset(RG_Seu, idents = c(1,16,10)) RG02Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG02) & RG_UMAP$UMAP_1 > 0 & RG_UMAP$UMAP_2 > 0 & RG_UMAP$UMAP_2 < 10) RG03 = subset(RG_Seu, idents = c(2,7)) RG03Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG03) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 > 2 & RG_UMAP$UMAP_2 < 9) RG04 = subset(RG_Seu, idents = c(3,4,14)) RG04Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG04) & RG_UMAP$UMAP_1 > -1 & RG_UMAP$UMAP_1 < 7 & RG_UMAP$UMAP_2 > -9 & RG_UMAP$UMAP_2 < 1) RG05 = subset(RG_Seu, idents = c(6)) RG05Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG05) &RG_UMAP$UMAP_2 > 12) RG06 = subset(RG_Seu, idents = c(8,19)) RG06Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG06) & RG_UMAP$UMAP_1 > -6 & RG_UMAP$UMAP_1 < -2 & RG_UMAP$UMAP_2 > -4 & RG_UMAP$UMAP_2 < 1 | row.names(RG_UMAP) %in% colnames(RG06) & RG_UMAP$UMAP_1 > -2 & RG_UMAP$UMAP_1 < 2 & RG_UMAP$UMAP_2 > -1 & RG_UMAP$UMAP_2 < 2) RG07 = subset(RG_Seu, idents = c(9,11,20)) RG07Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG07) & RG_UMAP$UMAP_1 > 2 & RG_UMAP$UMAP_2 < -4) RG08 = subset(RG_Seu, idents = c(12,15)) RG08Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG08) & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > 2) RG09 = subset(RG_Seu, idents = c(13,18)) RG09Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG09) & RG_UMAP$UMAP_1 > -3.2 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 < -4) RG10 = subset(RG_Seu, idents = c(17)) RG10Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG10) & RG_UMAP$UMAP_1 > -9 & RG_UMAP$UMAP_1 < -5 & RG_UMAP$UMAP_2 < -5.5) RG11 = subset(RG_Seu, idents = 21) RG11Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG11) & RG_UMAP$UMAP_2 > 10 & RG_UMAP$UMAP_2 < 14) RG12 = subset(RG_Seu, idents = 22) RG12Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG12) & RG_UMAP$UMAP_1 < -5 & RG_UMAP$UMAP_2 < 0) RG13 = subset(RG_Seu, idents = 23) RG13Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG13) & RG_UMAP$UMAP_1 < -5 ) RG14 = subset(RG_Seu, idents = 24) RG14Clean = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG14) & RG_UMAP$UMAP_2 < 0) #CheckInput = RG14Clean #CheckUMAP(RG_Seu) RG_REST = subset(RG_Seu, cells = c(row.names(RG01Clean), row.names(RG02Clean), row.names(RG03Clean), row.names(RG04Clean), row.names(RG05Clean), row.names(RG06Clean), row.names(RG07Clean), row.names(RG08Clean), row.names(RG09Clean), row.names(RG10Clean), row.names(RG11Clean), row.names(RG12Clean), row.names(RG13Clean), row.names(RG14Clean), row.names(RG15Clean)), invert=T) #CheckInput = RG02_Extras #CheckUMAP(RG_Seu) RG01_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > -6 & RG_UMAP$UMAP_2 < -4 | row.names(RG_UMAP) %in% row.names(RG01Clean)) RG02_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > 3 & RG_UMAP$UMAP_2 > 1.2 & RG_UMAP$UMAP_2 < 10 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -1 & RG_UMAP$UMAP_2 > 1 & RG_UMAP$UMAP_2 < 4 & RG_UMAP$UMAP_2 < 3 | row.names(RG_UMAP) %in% row.names(RG02Clean)) RG03_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 > 3 & RG_UMAP$UMAP_2 < 9 & ! row.names(RG_UMAP) %in% c(row.names(RG02_Extras)) | row.names(RG_UMAP) %in% row.names(RG03Clean)) RG05_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_2 > 13.5 | row.names(RG_UMAP) %in% row.names(RG05Clean)) RG06_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -5.5 & RG_UMAP$UMAP_1 < -3 & RG_UMAP$UMAP_2 > -2.5 & RG_UMAP$UMAP_2 < 1.5 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -3 & RG_UMAP$UMAP_1 < 0 & RG_UMAP$UMAP_2 > -3.5 & RG_UMAP$UMAP_2 < 2 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -2 & RG_UMAP$UMAP_1 < 1.5 & RG_UMAP$UMAP_2 > -1 & RG_UMAP$UMAP_2 < 2 & ! row.names(RG_UMAP) %in% row.names(RG02_Extras) | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -7.7 & RG_UMAP$UMAP_1 < -5.5 & RG_UMAP$UMAP_2 > -1 & RG_UMAP$UMAP_2 < 1 | row.names(RG_UMAP) %in% row.names(RG06Clean)) RG07_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > 3.5 & RG_UMAP$UMAP_2 < -4| row.names(RG_UMAP) %in% row.names(RG07Clean)) RG08_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 < -11 & RG_UMAP$UMAP_2 > 2 | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -11 & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > 4| row.names(RG_UMAP) %in% row.names(RG08Clean)) RG09_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -2.5 & RG_UMAP$UMAP_1 < 1 & RG_UMAP$UMAP_2 < -5 & ! row.names(RG_UMAP) %in% row.names(RG07_Extras) | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > 1 & RG_UMAP$UMAP_1 < 5 & RG_UMAP$UMAP_2 < -8 & ! row.names(RG_UMAP) %in% row.names(RG07_Extras)| row.names(RG_UMAP) %in% row.names(RG09Clean)) RG11_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_2 > 10 & ! row.names(RG_UMAP) %in% c(row.names(RG05_Extras)) | row.names(RG_UMAP) %in% row.names(RG11Clean)) RG14_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -5 & RG_UMAP$UMAP_1 < -3 & RG_UMAP$UMAP_2 < -8 | row.names(RG_UMAP) %in% row.names(RG14Clean)) RG10_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -9 & RG_UMAP$UMAP_1 < -4.7 & RG_UMAP$UMAP_2 < -4 & ! row.names(RG_UMAP) %in% c(row.names(RG01_Extras), row.names(RG14_Extras)) | row.names(RG_UMAP) %in% row.names(RG10Clean)) RG12_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 < -5 & RG_UMAP$UMAP_2 < 0 & ! row.names(RG_UMAP) %in% c(row.names(RG01_Extras), row.names(RG06_Extras), row.names(RG10_Extras)) | row.names(RG_UMAP) %in% row.names(RG12Clean)) RG15_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -7 & RG_UMAP$UMAP_1 < 0 & RG_UMAP$UMAP_2 < -1 & RG_UMAP$UMAP_2 > -8 & ! row.names(RG_UMAP) %in% c(row.names(RG06_Extras), row.names(RG08_Extras), row.names(RG10_Extras), row.names(RG09_Extras), row.names(RG12_Extras)) | row.names(RG_UMAP) %in% row.names(RG15Clean)) RG13_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -12 & RG_UMAP$UMAP_1 < -7 & RG_UMAP$UMAP_2 > -0.5 & RG_UMAP$UMAP_2 < 5 & ! row.names(RG_UMAP) %in% c(row.names(RG06_Extras), row.names(RG08_Extras), row.names(RG12_Extras)) | row.names(RG_UMAP) %in% row.names(RG13Clean)) RG04_Extras = subset(RG_UMAP, row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -0.5 & RG_UMAP$UMAP_1 < 8 & RG_UMAP$UMAP_2 > -4 & RG_UMAP$UMAP_2 < 1.2 & ! row.names(RG_UMAP) %in% c(row.names(RG02_Extras), row.names(RG06_Extras), row.names(RG07_Extras), row.names(RG09_Extras), row.names(RG15_Extras)) | row.names(RG_UMAP) %in% colnames(RG_REST) & RG_UMAP$UMAP_1 > -0.5 & RG_UMAP$UMAP_1 < 6 & RG_UMAP$UMAP_2 > -8 & RG_UMAP$UMAP_2 < 1.2 & ! row.names(RG_UMAP) %in% c(row.names(RG02_Extras), row.names(RG06_Extras), row.names(RG07_Extras), row.names(RG09_Extras), row.names(RG15_Extras)) | row.names(RG_UMAP) %in% row.names(RG04Clean)) #CheckInput = RG06_Extras #CheckUMAP(RG_Seu) RG_Assigns = GenerateMetaData_Barcodes(list("RG_01" = RG01_Extras, "RG_02" = RG02_Extras, "RG_03" = RG03_Extras, "RG_04" = RG04_Extras, "RG_05" = RG05_Extras, "RG_06" = RG06_Extras, "RG_07" = RG07_Extras, "RG_08" = RG08_Extras, "RG_09" = RG09_Extras, "RG_10" = RG10_Extras, "RG_11" = RG11_Extras, "RG_12" = RG12_Extras, "RG_13" = RG13_Extras, "RG_14" = RG14_Extras, "RG_15" = RG15_Extras)) RG02_Extras_Pt2 = subset(RG_Seu, cells = RG_Assigns$Barcodes, invert=T) #CheckInput = RG02_Extras_Pt2 #CheckUMAP(RG_Seu) RG_Assigns$Dups = duplicated(RG_Assigns$Barcodes) | duplicated(RG_Assigns$Barcodes, fromLast=T) RG_Assigns_T = subset(RG_Assigns, RG_Assigns$Dups == T) unique(RG_Assigns_T$Pop) RG_Assigns = GenerateMetaData(list( "RG_01" = RG01_Extras, "RG_02" = RG02_Extras, "RG_03" = RG03_Extras, "RG_04" = RG04_Extras, "RG_05" = RG05_Extras, "RG_06" = RG06_Extras, "RG_07" = RG07_Extras, "RG_08" = RG08_Extras, "RG_09" = RG09_Extras, "RG_10" = RG10_Extras, "RG_11" = RG11_Extras, "RG_12" = RG12_Extras, "RG_13" = RG13_Extras, "RG_14" = RG14_Extras, "RG_15" = RG15_Extras, "RG_06" = RG02_Extras_Pt2)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Neurepithelial
Idents(KaZhouAll) = "MainClusters" NE_Seu = subset(KaZhouAll, idents = "Neuroepithelial") DefaultAssay(NE_Seu) = "integrated" NE_Seu = FindVariableFeatures(NE_Seu) NE_Seu = ScaleData(NE_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(NE_Seu) = "integrated" NE_Seu = RunPCA(NE_Seu, npcs = 20) #NE_Seu <- RunHarmony(NE_Seu, group.by.vars = "Timepoint2") NE_Seu <- RunUMAP(NE_Seu, dims = 1:20, spread= 2)#, reduction = "harmony") DefaultAssay(NE_Seu) = "RNA" ##### set.dim = c(20) set.res = c(1) set.kparam = c(100) #ClusterFunc_All_RNA(NE_Seu) DefaultAssay(NE_Seu) = "integrated" NE_Seu <- FindNeighbors(NE_Seu, k.param=100, dims=1:20) NE_Seu <- FindClusters(NE_Seu, resolution = 1) DefaultAssay(NE_Seu) = "RNA" #CheckInput = NE09 #CheckUMAP(NE_Seu) NE_UMAP = as.data.frame(NE_Seu@reductions$umap@cell.embeddings) NE01 = subset(NE_Seu, idents = c(0,4,8)) NE01Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE01) & NE_UMAP$UMAP_2 > 1 & NE_UMAP$UMAP_1 > -2) NE02 = subset(NE_Seu, idents = c(1)) NE02Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE02) & NE_UMAP$UMAP_1 > 5 & NE_UMAP$UMAP_2 < 5 | row.names(NE_UMAP) %in% colnames(NE02) & NE_UMAP$UMAP_1 > 1 & NE_UMAP$UMAP_2 > -5 & NE_UMAP$UMAP_2 < 5) NE03 = subset(NE_Seu, idents = c(2,9)) NE03Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE03) & NE_UMAP$UMAP_1 > -13 & NE_UMAP$UMAP_1 < -4 & NE_UMAP$UMAP_2 > -12 & NE_UMAP$UMAP_2 < 5) NE04 = subset(NE_Seu, idents = c(3,7,11)) NE04Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE04) & NE_UMAP$UMAP_1 > -8 & NE_UMAP$UMAP_1 < 8 & NE_UMAP$UMAP_2 > -7 & NE_UMAP$UMAP_2 < 2) NE05 = subset(NE_Seu, idents = c(5)) NE05Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE05) &NE_UMAP$UMAP_2 < -11) NE06 = subset(NE_Seu, idents = c(6)) NE06Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE06) & NE_UMAP$UMAP_1 > -10 & NE_UMAP$UMAP_1 < 7 & NE_UMAP$UMAP_2 > -11 & NE_UMAP$UMAP_2 < -3 ) NE07 = subset(NE_Seu, idents = c(10)) NE07Clean = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE07) & NE_UMAP$UMAP_2 > 11) NE08 = subset(NE_Seu, idents = c(12)) NE09 = subset(NE_Seu, idents = c(13,14)) NE_REST = subset(NE_Seu, cells = c(row.names(NE01Clean), row.names(NE02Clean), row.names(NE03Clean), row.names(NE04Clean), row.names(NE05Clean), row.names(NE06Clean), row.names(NE07Clean), colnames(NE08), colnames(NE09)), invert=T) #CheckInput = NE06_Extras #CheckUMAP(NE_Seu) NE01_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > -5 & NE_UMAP$UMAP_2 > 3 & NE_UMAP$UMAP_2 < 13 | row.names(NE_UMAP) %in% row.names(NE01Clean)) NE02_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > 5 & NE_UMAP$UMAP_2 < -2 | row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > 3 & NE_UMAP$UMAP_2 > -6 & NE_UMAP$UMAP_2 < 2 | row.names(NE_UMAP) %in% row.names(NE02Clean)) NE03_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > -12 & NE_UMAP$UMAP_1 < -6 & NE_UMAP$UMAP_2 > -12 & NE_UMAP$UMAP_2 < 5 | row.names(NE_UMAP) %in% row.names(NE03Clean)) NE04_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST)& NE_UMAP$UMAP_1 > -8 & NE_UMAP$UMAP_1 < 8 & NE_UMAP$UMAP_2 > -7 & NE_UMAP$UMAP_2 < 3 & ! row.names(NE_UMAP) %in% c(row.names(NE03_Extras), row.names(NE02_Extras), row.names(NE01_Extras)) | row.names(NE_UMAP) %in% row.names(NE04Clean)) #& ! row.names(NE_UMAP) %in% c(row.names(NE02_Extras)) NE05_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_2 < -12 | row.names(NE_UMAP) %in% row.names(NE05Clean)) NE06_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST)& NE_UMAP$UMAP_1 > -10 & NE_UMAP$UMAP_1 < 7 & NE_UMAP$UMAP_2 > -11 & NE_UMAP$UMAP_2 < -3 & ! row.names(NE_UMAP) %in% c(row.names(NE03_Extras), row.names(NE02_Extras), row.names(NE04_Extras))| row.names(NE_UMAP) %in% row.names(NE06Clean)) NE07_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 > 5 & NE_UMAP$UMAP_2 > 12 | row.names(NE_UMAP) %in% row.names(NE07Clean)) NE08_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 < 0 & NE_UMAP$UMAP_2 > 10 | row.names(NE_UMAP) %in% colnames(NE08)) NE09_Extras = subset(NE_UMAP, row.names(NE_UMAP) %in% colnames(NE_REST) & NE_UMAP$UMAP_1 < -12 | row.names(NE_UMAP) %in% colnames(NE09)) #CheckInput = NE09_Extras #CheckUMAP(NE_Seu) NE_Assigns = GenerateMetaData_Barcodes(list("NE_01" = NE01_Extras, "NE_02" = NE02_Extras, "NE_03" = NE03_Extras, "NE_04" = NE04_Extras, "NE_05" = NE05_Extras, "NE_06" = NE06_Extras, "NE_07" = NE07_Extras, "NE_08" = NE08_Extras, "NE_09" = NE09_Extras)) #NE02_Extras_Pt2 = subset(NE_Seu, cells = NE_Assigns$Barcodes, invert=T) #CheckInput = NE02_Extras_Pt2 #CheckUMAP(NE_Seu) NE_Assigns$Dups = duplicated(NE_Assigns$Barcodes) | duplicated(NE_Assigns$Barcodes, fromLast=T) NE_Assigns_T = subset(NE_Assigns, NE_Assigns$Dups == T) unique(NE_Assigns_T$Pop) NE_Assigns = GenerateMetaData(list( "NE_01" = NE01_Extras, "NE_02" = NE02_Extras, "NE_03" = NE03_Extras, "NE_04" = NE04_Extras, "NE_05" = NE05_Extras, "NE_06" = NE06_Extras, "NE_07" = NE07_Extras, "NE_08" = NE08_Extras, "NE_09" = NE09_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Intermediate Progenitors
Idents(KaZhouAll) = "MainClusters" IP_Seu = subset(KaZhouAll, idents = "IntProgen") DefaultAssay(IP_Seu) = "integrated" IP_Seu = FindVariableFeatures(IP_Seu) IP_Seu = ScaleData(IP_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(IP_Seu) = "integrated" IP_Seu = RunPCA(IP_Seu, npcs = 30) IP_Seu <- RunHarmony(IP_Seu, group.by.vars = "Timepoint2") IP_Seu <- RunUMAP(IP_Seu, dims = 1:30, spread= 5, reduction = "harmony") DefaultAssay(IP_Seu) = "RNA" ##### set.dim = c(30) set.res = c(1) set.kparam = c(20,50, 100) #ClusterFunc_All_RNA(IP_Seu) DefaultAssay(IP_Seu) = "integrated" IP_Seu <- FindNeighbors(IP_Seu, k.param=50, dims=1:30) IP_Seu <- FindClusters(IP_Seu, resolution = 1) DefaultAssay(IP_Seu) = "RNA" #CheckInput = IP01Clean #CheckUMAP(IP_Seu) IP_UMAP = as.data.frame(IP_Seu@reductions$umap@cell.embeddings) IP01 = subset(IP_Seu, idents = c(0,1,7,9)) IP01Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP01) & IP_UMAP$UMAP_2 < 0 & IP_UMAP$UMAP_1 < -5 | row.names(IP_UMAP) %in% colnames(IP01) & IP_UMAP$UMAP_2 > -3 & IP_UMAP$UMAP_2 < 3 & IP_UMAP$UMAP_1 > -20 & IP_UMAP$UMAP_1 < 3 | row.names(IP_UMAP) %in% colnames(IP01) & IP_UMAP$UMAP_2 > 3 & IP_UMAP$UMAP_2 < 14 & IP_UMAP$UMAP_1 > -15 & IP_UMAP$UMAP_1 < 7 ) IP02 = subset(IP_Seu, idents = c(2,3,5)) IP02Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP02) & IP_UMAP$UMAP_1 > -7 & IP_UMAP$UMAP_2 < -5 | row.names(IP_UMAP) %in% colnames(IP02) & IP_UMAP$UMAP_1 > 5 & IP_UMAP$UMAP_2 < 5| row.names(IP_UMAP) %in% colnames(IP02) & IP_UMAP$UMAP_1 > 10) IP03 = subset(IP_Seu, idents = c(4,8)) IP03Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP03) & IP_UMAP$UMAP_1 < 3 & IP_UMAP$UMAP_2 > 10) IP04 = subset(IP_Seu, idents = c(6)) IP04Clean = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP04) & IP_UMAP$UMAP_1 < -20) IP_REST = subset(IP_Seu, cells = c(row.names(IP01Clean), row.names(IP02Clean), row.names(IP03Clean), row.names(IP04Clean)), invert=T) #CheckInput = IP01_Extras #CheckUMAP(IP_Seu) IP01_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_2 < 0 & IP_UMAP$UMAP_1 < -5 | row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_2 > -5 & IP_UMAP$UMAP_2 < 4 & IP_UMAP$UMAP_1 > -20 & IP_UMAP$UMAP_1 < 3 | row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_2 > 2 & IP_UMAP$UMAP_2 < 14 & IP_UMAP$UMAP_1 > -15 & IP_UMAP$UMAP_1 < 5 | row.names(IP_UMAP) %in% row.names(IP01Clean)) IP02_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 > -7 & IP_UMAP$UMAP_2 < -5 |row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 > 4 & IP_UMAP$UMAP_2 < 5| row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 > 10 | row.names(IP_UMAP) %in% row.names(IP02Clean)) IP03_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 < 3 & IP_UMAP$UMAP_2 > 10 & ! row.names(IP_UMAP) %in% row.names(IP01_Extras) | row.names(IP_UMAP) %in% row.names(IP03Clean)) IP04_Extras = subset(IP_UMAP, row.names(IP_UMAP) %in% colnames(IP_REST) & IP_UMAP$UMAP_1 < -20 & IP_UMAP$UMAP_2 > 0 | row.names(IP_UMAP) %in% row.names(IP04Clean)) #CheckInput = IP04_Extras #CheckUMAP(IP_Seu) IP_Assigns = GenerateMetaData_Barcodes(list("IP_01" = IP01_Extras, "IP_02" = IP02_Extras, "IP_03" = IP03_Extras, "IP_04" = IP04_Extras)) #IP02_Extras_Pt2 = subset(IP_Seu, cells = IP_Assigns$Barcodes, invert=T) #CheckInput = IP02_Extras_Pt2 #CheckUMAP(IP_Seu) IP_Assigns$Dups = duplicated(IP_Assigns$Barcodes) | duplicated(IP_Assigns$Barcodes, fromLast=T) IP_Assigns_T = subset(IP_Assigns, IP_Assigns$Dups == T) unique(IP_Assigns_T$Pop) IP_Assigns = GenerateMetaData(list( "IP_01" = IP01_Extras, "IP_02" = IP02_Extras, "IP_03" = IP03_Extras, "IP_04" = IP04_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Dividing Progenitors
Idents(KaZhouAll) = "MainClusters" Div_Seu = subset(KaZhouAll, idents = "Dividing") DefaultAssay(Div_Seu) = "integrated" Div_Seu = FindVariableFeatures(Div_Seu) Div_Seu = ScaleData(Div_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Div_Seu) = "integrated" Div_Seu = RunPCA(Div_Seu, npcs = 20) #Div_Seu <- RunHarmony(Div_Seu, group.by.vars = "Timepoint2") Div_Seu <- RunUMAP(Div_Seu, dims = 1:20, spread= 2)#, reduction = "harmony") DefaultAssay(Div_Seu) = "RNA" ##### set.dim = c(20) set.res = c(1) set.kparam = c(50, 20) #ClusterFunc_All_RNA(Div_Seu) DefaultAssay(Div_Seu) = "integrated" Div_Seu <- FindNeighbors(Div_Seu, k.param=20, dims=1:20) Div_Seu <- FindClusters(Div_Seu, resolution = 1) DefaultAssay(Div_Seu) = "RNA" #CheckInput = Div02Clean #CheckUMAP(Div_Seu) Div_UMAP = as.data.frame(Div_Seu@reductions$umap@cell.embeddings) Div01 = subset(Div_Seu, idents = c(0)) #Neuro-Astro Div01Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div01) & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -1 & Div_UMAP$UMAP_2 < 3 | row.names(Div_UMAP) %in% colnames(Div01) & Div_UMAP$UMAP_1 > -5 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -3 & Div_UMAP$UMAP_2 < 1) Div02 = subset(Div_Seu, idents = c(1)) #Neuro Div02Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div02) & Div_UMAP$UMAP_1 > -11 & Div_UMAP$UMAP_1 < -6 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 2 | row.names(Div_UMAP) %in% colnames(Div02) & Div_UMAP$UMAP_1 > -14 & Div_UMAP$UMAP_1 < -11 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 1.) Div03 = subset(Div_Seu, idents = c(2,16)) #No lineage Div03Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div03) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_2 < -9 | row.names(Div_UMAP) %in% colnames(Div03) & Div_UMAP$UMAP_1 > 5 & Div_UMAP$UMAP_2 < 0) Div04 = subset(Div_Seu, idents = c(3)) #Astro Div04Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div04) & Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_1 < 9) Div05 = subset(Div_Seu, idents = c(4)) #Fibroblasts Div05Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div05) & Div_UMAP$UMAP_1 > 6 & Div_UMAP$UMAP_2 >7) Div06 = subset(Div_Seu, idents = c(5)) #Fibroblasts Div06Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div06) & Div_UMAP$UMAP_1 > -6 & Div_UMAP$UMAP_1 < 4 & Div_UMAP$UMAP_2 > 4 & Div_UMAP$UMAP_2 < 9) Div07 = subset(Div_Seu, idents = c(6)) #Fibroblasts Div07Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div07) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_1 < 10 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 5) Div08 = subset(Div_Seu, idents = c(7)) #Neuro Div08Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div08) & Div_UMAP$UMAP_2 < -10 & Div_UMAP$UMAP_1 < 8) Div09 = subset(Div_Seu, idents = c(8)) #Fibroblasts Div09Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div09) & Div_UMAP$UMAP_1 > 5 & Div_UMAP$UMAP_1 < 11 & Div_UMAP$UMAP_2 > 1.7 & Div_UMAP$UMAP_2 < 10) Div10 = subset(Div_Seu, idents = c(9)) #Neuro Div10Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div10) & Div_UMAP$UMAP_1 > 0.5 & Div_UMAP$UMAP_1 < 7.5 & Div_UMAP$UMAP_2 > 0 & Div_UMAP$UMAP_2 < 9) Div11 = subset(Div_Seu, idents = c(10,15,23)) Div11Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div11) & Div_UMAP$UMAP_1 > -7 & Div_UMAP$UMAP_1 < 0 & Div_UMAP$UMAP_2 > 6 & Div_UMAP$UMAP_2 < 10.5) #No Lineage Div12Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div11) & Div_UMAP$UMAP_1 < -7 & Div_UMAP$UMAP_2 > -1 & Div_UMAP$UMAP_2 < 7) #Fibroblasts Div13 = subset(Div_Seu, idents = c(11)) #Neuro-Olig Div13Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div13) & Div_UMAP$UMAP_1 < -10 & Div_UMAP$UMAP_2 < -2) Div14 = subset(Div_Seu, idents = c(12)) Div14Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div14) & Div_UMAP$UMAP_1 > -1& Div_UMAP$UMAP_1 < 2.5 & Div_UMAP$UMAP_2 < 2) #Neuro Div15Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div14) & Div_UMAP$UMAP_1 > 2.5 & Div_UMAP$UMAP_2 < 2) #No lineage Div16 = subset(Div_Seu, idents = c(13,20)) #Neuro-Astro Div16Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div16) & Div_UMAP$UMAP_1 > -13 & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_2 < 0) Div17 = subset(Div_Seu, idents = c(14, 18)) #Fibroblasts Div17Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div17) & Div_UMAP$UMAP_1 > -1 & Div_UMAP$UMAP_2 > 2) Div18 = subset(Div_Seu, idents = c(17)) #TAGLN/DCN Div18Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div18) & Div_UMAP$UMAP_2 > 10) Div19 = subset(Div_Seu, idents = c(19)) #Astro Div19Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div19) & Div_UMAP$UMAP_1 > -9 & Div_UMAP$UMAP_1 < 0 & Div_UMAP$UMAP_2 < -2) Div20 = subset(Div_Seu, idents = c(21)) #Olig Div20Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div20) & Div_UMAP$UMAP_1 > -9 & Div_UMAP$UMAP_1 < 0) Div21 = subset(Div_Seu, idents = c(22)) #Astro Div21Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div21) & Div_UMAP$UMAP_2 < -5) Div22 = subset(Div_Seu, idents = c(24)) #Fibroblasts Div23 = subset(Div_Seu, idents = c(25)) #Fibroblasts Div23Clean = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div23) & Div_UMAP$UMAP_1 > 11) #CheckInput = Div23Clean #CheckUMAP(Div_Seu) Div_REST = subset(Div_Seu, cells = c(row.names(Div01Clean), row.names(Div02Clean), row.names(Div03Clean), row.names(Div04Clean), row.names(Div05Clean), row.names(Div06Clean), row.names(Div07Clean), row.names(Div08Clean), row.names(Div09Clean), row.names(Div10Clean), row.names(Div11Clean), row.names(Div12Clean), row.names(Div13Clean), row.names(Div14Clean), row.names(Div15Clean), row.names(Div16Clean), row.names(Div17Clean), row.names(Div18Clean), row.names(Div19Clean), row.names(Div20Clean), row.names(Div21Clean),colnames(Div22), row.names(Div23Clean)), invert=T) #CheckInput = Div02_Extras #CheckUMAP(Div_Seu) Div01_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -6 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -1 & Div_UMAP$UMAP_2 < 3 | row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -5 & Div_UMAP$UMAP_1 < 0.5 & Div_UMAP$UMAP_2 > -3 & Div_UMAP$UMAP_2 < 1 | row.names(Div_UMAP) %in% row.names(Div01Clean)) Div02_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_2 > -3 & Div_UMAP$UMAP_2 < 0 & ! row.names(Div_UMAP) %in% row.names(Div01_Extras)| row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -13 & Div_UMAP$UMAP_1 < -6 & Div_UMAP$UMAP_2 > -3.5 & Div_UMAP$UMAP_2 < 2 | row.names(Div_UMAP) %in% row.names(Div02Clean)) Div03_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_2 < -9 | row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 7 & Div_UMAP$UMAP_2 < 0 | row.names(Div_UMAP) %in% row.names(Div03Clean)) Div05_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 6 & Div_UMAP$UMAP_2 > 9 | row.names(Div_UMAP) %in% row.names(Div05Clean)) Div07_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_1 < 10 & Div_UMAP$UMAP_2 > -4 & Div_UMAP$UMAP_2 < 3 & ! row.names(Div_UMAP) %in% row.names(Div03_Extras) | row.names(Div_UMAP) %in% row.names(Div07Clean)) Div08_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 3 & Div_UMAP$UMAP_2 < -13 & ! row.names(Div_UMAP) %in% row.names(Div03_Extras) | row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_1 < 3 & Div_UMAP$UMAP_2 < -11 & ! row.names(Div_UMAP) %in% row.names(Div03_Extras) | row.names(Div_UMAP) %in% row.names(Div08Clean)) Div09_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 7 & Div_UMAP$UMAP_1 < 11 & Div_UMAP$UMAP_2 > 2 & Div_UMAP$UMAP_2 < 7 & ! row.names(Div_UMAP) %in% row.names(Div07_Extras)| row.names(Div_UMAP) %in% row.names(Div09Clean)) Div10_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 >1 & Div_UMAP$UMAP_1 < 6 & Div_UMAP$UMAP_2 > 2 & Div_UMAP$UMAP_2 < 6 | row.names(Div_UMAP) %in% row.names(Div10Clean)) Div12_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 < -8 & Div_UMAP$UMAP_2 > -2 & Div_UMAP$UMAP_2 < 9 & ! row.names(Div_UMAP) %in% row.names(Div02_Extras) | row.names(Div_UMAP) %in% row.names(Div12Clean)) Div13_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 < -10 & Div_UMAP$UMAP_2 < -2 & ! row.names(Div_UMAP) %in% row.names(Div02_Extras) | row.names(Div_UMAP) %in% row.names(Div13Clean)) Div14_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -0.5 & Div_UMAP$UMAP_1 < 2.5 & Div_UMAP$UMAP_2 < 0 & Div_UMAP$UMAP_2 > -6 & ! row.names(Div_UMAP) %in% row.names(Div01_Extras) | row.names(Div_UMAP) %in% row.names(Div14Clean)) Div15_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 2.5 & Div_UMAP$UMAP_1 < 7.5 & Div_UMAP$UMAP_2 < -2 & Div_UMAP$UMAP_2 > -7 & ! row.names(Div_UMAP) %in% c(row.names(Div03_Extras), row.names(Div07_Extras)) | row.names(Div_UMAP) %in% row.names(Div15Clean)) Div04_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST)& Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_2 > -10 & Div_UMAP$UMAP_2 < -5 & ! row.names(Div_UMAP) %in% c(row.names(Div03_Extras), row.names(Div14_Extras), row.names(Div15_Extras)) | row.names(Div_UMAP) %in% row.names(Div04Clean)) Div16_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -12 & Div_UMAP$UMAP_1 < -3 & Div_UMAP$UMAP_2 < -3 & Div_UMAP$UMAP_2 > -6 & ! row.names(Div_UMAP) %in% c(row.names(Div02_Extras), row.names(Div13_Extras)) | row.names(Div_UMAP) %in% row.names(Div16Clean)) Div17_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 1 & Div_UMAP$UMAP_1 < 10 & Div_UMAP$UMAP_2 < 11 & Div_UMAP$UMAP_2 > 6 & ! row.names(Div_UMAP) %in% c(row.names(Div05_Extras), row.names(Div09_Extras)) | row.names(Div_UMAP) %in% row.names(Div17Clean)) Div18_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_1 < 3 & Div_UMAP$UMAP_2 > 10.5 | row.names(Div_UMAP) %in% row.names(Div18Clean)) Div19_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -8 & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_2 < -3 & ! row.names(Div_UMAP) %in% c(row.names(Div02_Extras), row.names(Div16_Extras))| row.names(Div_UMAP) %in% row.names(Div19Clean)) Div20_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -9 & Div_UMAP$UMAP_1 < -2 & Div_UMAP$UMAP_2 > 2 & Div_UMAP$UMAP_2 < 5 & ! row.names(Div_UMAP) %in% c(row.names(Div01_Extras), row.names(Div05_Extras))| row.names(Div_UMAP) %in% row.names(Div20Clean)) Div06_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > -7 & Div_UMAP$UMAP_1 < 3 & Div_UMAP$UMAP_2 > 4 & Div_UMAP$UMAP_2 < 9 & ! row.names(Div_UMAP) %in% c(row.names(Div10_Extras), row.names(Div20_Extras)) | row.names(Div_UMAP) %in% row.names(Div06Clean)) Div11_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 >-7 & Div_UMAP$UMAP_1 < 0 & Div_UMAP$UMAP_2 > 5 & Div_UMAP$UMAP_2 < 11 & ! row.names(Div_UMAP) %in% row.names(Div06_Extras) | row.names(Div_UMAP) %in% row.names(Div11Clean)) Div21_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 0 & Div_UMAP$UMAP_2 < -9 & ! row.names(Div_UMAP) %in% c(row.names(Div03_Extras), row.names(Div04_Extras), row.names(Div08_Extras))| row.names(Div_UMAP) %in% row.names(Div21Clean)) Div22_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 9 & Div_UMAP$UMAP_1 < 11 & Div_UMAP$UMAP_2 > 6 & Div_UMAP$UMAP_2 < 8 & ! row.names(Div_UMAP) %in% c(row.names(Div07_Extras), row.names(Div17_Extras), row.names(Div05_Extras))| row.names(Div_UMAP) %in% colnames(Div22)) Div23_Extras = subset(Div_UMAP, row.names(Div_UMAP) %in% colnames(Div_REST) & Div_UMAP$UMAP_1 > 11 | row.names(Div_UMAP) %in% row.names(Div23Clean)) #CheckInput = Div07_Extras #CheckUMAP(Div_Seu) Div_Assigns = GenerateMetaData_Barcodes(list("Div_01" = Div01_Extras, "Div_02" = Div02_Extras, "Div_03" = Div03_Extras, "Div_04" = Div04_Extras, "Div_05" = Div05_Extras, "Div_06" = Div06_Extras, "Div_07" = Div07_Extras, "Div_08" = Div08_Extras, "Div_09" = Div09_Extras, "Div_10" = Div10_Extras, "Div_11" = Div11_Extras, "Div_12" = Div12_Extras, "Div_13" = Div13_Extras, "Div_14" = Div14_Extras, "Div_15" = Div15_Extras, "Div_16" = Div16_Extras, "Div_17" = Div17_Extras, "Div_18" = Div18_Extras, "Div_19" = Div19_Extras, "Div_20" = Div20_Extras, "Div_21" = Div21_Extras, "Div_22" = Div22_Extras, "Div_23" = Div23_Extras)) Div02_Extras_Pt2 = subset(Div_Seu, cells = Div_Assigns$Barcodes, invert=T) #CheckInput = Div02_Extras_Pt2 #CheckUMAP(Div_Seu) Div_Assigns$Dups = duplicated(Div_Assigns$Barcodes) | duplicated(Div_Assigns$Barcodes, fromLast=T) Div_Assigns_T = subset(Div_Assigns, Div_Assigns$Dups == T) unique(Div_Assigns_T$Pop) Div_Assigns = GenerateMetaData(list("Div_01" = Div01_Extras, "Div_02" = Div02_Extras, "Div_03" = Div03_Extras, "Div_04" = Div04_Extras, "Div_05" = Div05_Extras, "Div_06" = Div06_Extras, "Div_07" = Div07_Extras, "Div_08" = Div08_Extras, "Div_09" = Div09_Extras, "Div_10" = Div10_Extras, "Div_11" = Div11_Extras, "Div_12" = Div12_Extras, "Div_13" = Div13_Extras, "Div_14" = Div14_Extras, "Div_15" = Div15_Extras, "Div_16" = Div16_Extras, "Div_17" = Div17_Extras, "Div_18" = Div18_Extras, "Div_19" = Div19_Extras, "Div_20" = Div20_Extras, "Div_21" = Div21_Extras, "Div_22" = Div22_Extras, "Div_23" = Div23_Extras, "Div_10" = Div02_Extras_Pt2)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Astrocytes
Idents(KaZhouAll) = "MainClusters" Astro_Seu = subset(KaZhouAll, idents = "Astrocytes") DefaultAssay(Astro_Seu) = "integrated" Astro_Seu = FindVariableFeatures(Astro_Seu) Astro_Seu = ScaleData(Astro_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Astro_Seu) = "integrated" Astro_Seu = RunPCA(Astro_Seu, npcs = 20) #Astro_Seu <- RunHarmony(Astro_Seu, group.by.vars = "Timepoint2") Astro_Seu <- RunUMAP(Astro_Seu, dims = 1:20, spread= 2)#, reduction = "harmony") DefaultAssay(Astro_Seu) = "RNA" ##### set.dim = c(20) set.res = c(1) set.kparam = c(50,100) ##ClusterFunc_All_RNA(Astro_Seu) DefaultAssay(Astro_Seu) = "integrated" Astro_Seu <- FindNeighbors(Astro_Seu, k.param=100, dims=1:20) Astro_Seu <- FindClusters(Astro_Seu, resolution = 1) DefaultAssay(Astro_Seu) = "RNA" ##CheckInput = Astro06Clean #CheckUMAP(Astro_Seu) Astro_UMAP = as.data.frame(Astro_Seu@reductions$umap@cell.embeddings) Astro01 = subset(Astro_Seu, idents = c(4)) Astro01Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro01) & Astro_UMAP$UMAP_1 < 0 & Astro_UMAP$UMAP_2 > 1) Astro02 = subset(Astro_Seu, idents = c(2,11)) Astro02Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro02) & Astro_UMAP$UMAP_1 > 4 & Astro_UMAP$UMAP_2 > -5 & Astro_UMAP$UMAP_2 < 2.5) Astro03 = subset(Astro_Seu, idents = c(9)) Astro03Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro03) & Astro_UMAP$UMAP_1 > 0) Astro04 = subset(Astro_Seu, idents = c(8)) Astro04Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro04) & Astro_UMAP$UMAP_1 > 6) Astro05 = subset(Astro_Seu, idents = c(12)) Astro06 = subset(Astro_Seu, idents = c(0,1,3,5,6,7,10)) Astro06Clean = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro06) & Astro_UMAP$UMAP_1 < 0 & Astro_UMAP$UMAP_2 < -1.5 | row.names(Astro_UMAP) %in% colnames(Astro06) & Astro_UMAP$UMAP_1 > -7.5 & Astro_UMAP$UMAP_1 < 5 & Astro_UMAP$UMAP_2 > -4 & Astro_UMAP$UMAP_2 < 4 | row.names(Astro_UMAP) %in% colnames(Astro06) & Astro_UMAP$UMAP_1 > -4 & Astro_UMAP$UMAP_1 < 5.5 & Astro_UMAP$UMAP_2 > -5) Astro_REST = subset(Astro_Seu, cells = c(row.names(Astro01Clean), row.names(Astro02Clean), row.names(Astro03Clean), row.names(Astro04Clean), colnames(Astro05), row.names(Astro06Clean)), invert=T) #CheckInput = Astro05 #CheckUMAP(Astro_Seu) Astro01_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 < -1 & Astro_UMAP$UMAP_2 > 2 | row.names(Astro_UMAP) %in% row.names(Astro01Clean)) Astro02_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 > 8 & Astro_UMAP$UMAP_2 > 2.5 | row.names(Astro_UMAP) %in% row.names(Astro02Clean)) Astro03_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 > 3 & Astro_UMAP$UMAP_2 < -4.5 | row.names(Astro_UMAP) %in% row.names(Astro03Clean)) Astro04_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 > 4 & Astro_UMAP$UMAP_2 > -4.5 & Astro_UMAP$UMAP_2 < 2.5 | row.names(Astro_UMAP) %in% row.names(Astro04Clean)) Astro05_Extras = subset(Astro_UMAP, row.names(Astro_UMAP) %in% colnames(Astro_REST) & Astro_UMAP$UMAP_1 < -9 & Astro_UMAP$UMAP_2 > -2 & Astro_UMAP$UMAP_2 < 1 | row.names(Astro_UMAP) %in% colnames(Astro05)) #& ! row.names(Astro_UMAP) %in% c(row.names(Astro02_Extras)) #CheckInput = Astro05_Extras #CheckUMAP(Astro_Seu) Astro_Assigns = GenerateMetaData_Barcodes(list("Astro_01" = Astro01_Extras, "Astro_02" = Astro02_Extras, "Astro_03" = Astro03_Extras, "Astro_04" = Astro04_Extras, "Astro_05" = Astro05_Extras, "Astro_06" = Astro06Clean)) Astro02_Extras_Pt2 = subset(Astro_Seu, cells = Astro_Assigns$Barcodes, invert=T) CheckInput = Astro02_Extras_Pt2 CheckUMAP(Astro_Seu) Astro_Assigns$Dups = duplicated(Astro_Assigns$Barcodes) | duplicated(Astro_Assigns$Barcodes, fromLast=T) Astro_Assigns_T = subset(Astro_Assigns, Astro_Assigns$Dups == T) unique(Astro_Assigns_T$Pop) Astro_Assigns = GenerateMetaData(list( "Astro_01" = Astro01_Extras, "Astro_02" = Astro02_Extras, "Astro_03" = Astro03_Extras, "Astro_04" = Astro04_Extras, "Astro_05" = Astro05_Extras, "Astro_06" = Astro06Clean, "Astro_06" = Astro02_Extras_Pt2)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Endothelial cells
Idents(KaZhouAll) = "MainClusters" Endo_Seu = subset(KaZhouAll, idents = "Endothelial") DefaultAssay(Endo_Seu) = "integrated" Endo_Seu = FindVariableFeatures(Endo_Seu) Endo_Seu = ScaleData(Endo_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Endo_Seu) = "integrated" Endo_Seu = RunPCA(Endo_Seu, npcs = 30) #Endo_Seu <- RunHarmony(Endo_Seu, group.by.vars = "Timepoint2") Endo_Seu <- RunUMAP(Endo_Seu, dims = 1:30, spread= 5)#, reduction = "harmony") DefaultAssay(Endo_Seu) = "RNA" ##### set.dim = c(20) set.res = c(1) set.kparam = c(20) #ClusterFunc_All_RNA(Endo_Seu) DefaultAssay(Endo_Seu) = "integrated" Endo_Seu <- FindNeighbors(Endo_Seu, k.param=20, dims=1:20) Endo_Seu <- FindClusters(Endo_Seu, resolution = 1) DefaultAssay(Endo_Seu) = "RNA" #CheckInput = Endo08 #CheckUMAP(Endo_Seu) Endo_UMAP = as.data.frame(Endo_Seu@reductions$umap@cell.embeddings) Endo01 = subset(Endo_Seu, idents = c(0)) Endo01Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo01) & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 > -15 & Endo_UMAP$UMAP_2 < 3) Endo02 = subset(Endo_Seu, idents = c(1)) Endo02Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo02) & Endo_UMAP$UMAP_1 > -22 & Endo_UMAP$UMAP_1 < -2 & Endo_UMAP$UMAP_2 > -10 & Endo_UMAP$UMAP_2 < 12) Endo03 = subset(Endo_Seu, idents = c(3,4,11,12)) Endo03Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo03)& Endo_UMAP$UMAP_1 > 18 & Endo_UMAP$UMAP_2 < -2 | row.names(Endo_UMAP) %in% colnames(Endo03)& Endo_UMAP$UMAP_1 > 25) Endo04 = subset(Endo_Seu, idents = c(2)) Endo04Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo04) & Endo_UMAP$UMAP_1 < 0) Endo05 = subset(Endo_Seu, idents = c(5)) Endo05Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo05) & Endo_UMAP$UMAP_2 > 11) Endo06 = subset(Endo_Seu, idents = c(6,7)) Endo06Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo06) & Endo_UMAP$UMAP_1 > -2 & Endo_UMAP$UMAP_1 < 22) Endo07 = subset(Endo_Seu, idents = c(8)) Endo07Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo07) & Endo_UMAP$UMAP_1 < -10) Endo08 = subset(Endo_Seu, idents = c(9)) Endo08Clean = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo08) & Endo_UMAP$UMAP_1 < 5) Endo_REST = subset(Endo_Seu, cells = c(row.names(Endo01Clean), row.names(Endo02Clean), row.names(Endo03Clean), row.names(Endo04Clean), row.names(Endo05Clean), row.names(Endo06Clean), row.names(Endo07Clean), row.names(Endo08Clean)), invert=T) #CheckInput = Endo06_Extras #CheckUMAP(Endo_Seu) Endo01_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 > -13 & Endo_UMAP$UMAP_2 < 0 | row.names(Endo_UMAP) %in% row.names(Endo01Clean)) Endo02_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > -21 & Endo_UMAP$UMAP_1 < -2 & Endo_UMAP$UMAP_2 > 0 & Endo_UMAP$UMAP_2 < 11 | row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < -18 & Endo_UMAP$UMAP_2 > 9 | row.names(Endo_UMAP) %in% row.names(Endo02Clean)) Endo03_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > 18 & Endo_UMAP$UMAP_2 < -2 | row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > 25 | row.names(Endo_UMAP) %in% row.names(Endo03Clean)) Endo04_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 < -13 | row.names(Endo_UMAP) %in% row.names(Endo04Clean)) Endo05_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > -18 & Endo_UMAP$UMAP_1 < 0 & Endo_UMAP$UMAP_2 > 11 & Endo_UMAP$UMAP_2 < 23 | row.names(Endo_UMAP) %in% row.names(Endo05Clean)) Endo06_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 > -1 & Endo_UMAP$UMAP_2 > -15 & Endo_UMAP$UMAP_2 < 23 & ! row.names(Endo_UMAP) %in% row.names(Endo03_Extras) | row.names(Endo_UMAP) %in% row.names(Endo06Clean)) Endo07_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < -20 & Endo_UMAP$UMAP_2 > 0 & Endo_UMAP$UMAP_2 < 9 | row.names(Endo_UMAP) %in% row.names(Endo07Clean)) Endo08_Extras = subset(Endo_UMAP, row.names(Endo_UMAP) %in% colnames(Endo_REST) & Endo_UMAP$UMAP_1 < 10 & Endo_UMAP$UMAP_2 > 25 | row.names(Endo_UMAP) %in% row.names(Endo08Clean)) #CheckInput = Endo08_Extras #CheckUMAP(Endo_Seu) Endo_Assigns = GenerateMetaData_Barcodes(list("Endo_01" = Endo01_Extras, "Endo_02" = Endo02_Extras, "Endo_03" = Endo03_Extras, "Endo_04" = Endo04_Extras, "Endo_05" = Endo05_Extras, "Endo_06" = Endo06_Extras, "Endo_07" = Endo07_Extras, "Endo_08" = Endo08_Extras)) Endo02_Extras_Pt2 = subset(Endo_Seu, cells = Endo_Assigns$Barcodes, invert=T) CheckInput = Endo02_Extras_Pt2 CheckUMAP(Endo_Seu) Endo_Assigns$Dups = duplicated(Endo_Assigns$Barcodes) | duplicated(Endo_Assigns$Barcodes, fromLast=T) Endo_Assigns_T = subset(Endo_Assigns, Endo_Assigns$Dups == T) unique(Endo_Assigns_T$Pop) Endo_Assigns = GenerateMetaData(list("Vein_01" = Endo01_Extras, "Capillary_01" = Endo02_Extras, "Artery_01" = Endo03_Extras, "Arteriole" = Endo04_Extras, "Capillary_02" = Endo05_Extras, "Artery_02" = Endo06_Extras, "Vein_02" = Endo07_Extras, "Venule" = Endo08_Extras)) #save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Tanycytes
Idents(KaZhouAll) = "MainClusters" Tany_Seu = subset(KaZhouAll, idents = "Tanycytes") DefaultAssay(Tany_Seu) = "integrated" Tany_Seu = FindVariableFeatures(Tany_Seu) Tany_Seu = ScaleData(Tany_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Tany_Seu) = "integrated" Tany_Seu = RunPCA(Tany_Seu, npcs = 20) #Tany_Seu <- RunHarmony(Tany_Seu, group.by.vars = "Timepoint2") Tany_Seu <- RunUMAP(Tany_Seu, dims = 1:20, spread= 10)#, reduction = "harmony") DefaultAssay(Tany_Seu) = "RNA" ##### set.dim = c(20) set.res = c(1) set.kparam = c(15) #ClusterFunc_All_RNA(Tany_Seu) DefaultAssay(Tany_Seu) = "integrated" Tany_Seu <- FindNeighbors(Tany_Seu, k.param=15, dims=1:20) Tany_Seu <- FindClusters(Tany_Seu, resolution = 1) DefaultAssay(Tany_Seu) = "RNA" #CheckInput = Tany05Clean #CheckUMAP(Tany_Seu) Tany_UMAP = as.data.frame(Tany_Seu@reductions$umap@cell.embeddings) Tany01 = subset(Tany_Seu, idents = c(5)) #Alpha 1 Tany01Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany01) & Tany_UMAP$UMAP_1 < 18 & Tany_UMAP$UMAP_2 > 25) Tany02 = subset(Tany_Seu, idents = c(0,8)) #Alpha2 Tany02Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany02) & Tany_UMAP$UMAP_1 > -25 & Tany_UMAP$UMAP_1 < 10 & Tany_UMAP$UMAP_2 > -20 & Tany_UMAP$UMAP_2 < 20) Tany03 = subset(Tany_Seu, idents = c(1)) #Alpha 2 (Pt2) Tany03Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany03) & Tany_UMAP$UMAP_1 < 30 & Tany_UMAP$UMAP_2 > -10 & Tany_UMAP$UMAP_2 < 30) Tany04 = subset(Tany_Seu, idents = c(9)) #Beta1 Tany04Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany04) & Tany_UMAP$UMAP_1 < -20 & Tany_UMAP$UMAP_2 > 0) Tany05 = subset(Tany_Seu, idents = c(2,3)) #Beta2 Tany05Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany05) & Tany_UMAP$UMAP_1 > 25 & Tany_UMAP$UMAP_2 < 12 & Tany_UMAP$UMAP_2 > -18 | row.names(Tany_UMAP) %in% colnames(Tany05) & Tany_UMAP$UMAP_1 > 15 & Tany_UMAP$UMAP_2 < 0 & Tany_UMAP$UMAP_2 > -18) Tany06 = subset(Tany_Seu, idents = c(4)) Tany06Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany06) & Tany_UMAP$UMAP_1 > -25 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > -40 & Tany_UMAP$UMAP_2 < -5) Tany07 = subset(Tany_Seu, idents = c(6)) Tany07Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany07) & Tany_UMAP$UMAP_1 > -5 & Tany_UMAP$UMAP_2 > -31 & Tany_UMAP$UMAP_2 < -5) Tany08 = subset(Tany_Seu, idents = c(7)) #Rax+ Tany08Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany08) & Tany_UMAP$UMAP_1 > -22 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > 10 & Tany_UMAP$UMAP_2 < 25) Tany09 = subset(Tany_Seu, idents = c(10)) Tany09Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany09) & Tany_UMAP$UMAP_2 < -25) Tany10 = subset(Tany_Seu, idents = c(11)) #Rax+ Tany10Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany10) & Tany_UMAP$UMAP_2 > -20 & Tany_UMAP$UMAP_2 < -2) Tany11 = subset(Tany_Seu, idents = c(12)) Tany11Clean = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany11) & Tany_UMAP$UMAP_1 < -25) Tany12 = subset(Tany_Seu, idents = c(13)) #Rax+ #CheckInput = Tany05Clean #CheckUMAP(Tany_Seu) Tany_REST = subset(Tany_Seu, cells = c(row.names(Tany01Clean), row.names(Tany02Clean), row.names(Tany03Clean), row.names(Tany04Clean), row.names(Tany05Clean), row.names(Tany06Clean), row.names(Tany07Clean), row.names(Tany08Clean), row.names(Tany09Clean), row.names(Tany10Clean), row.names(Tany11Clean), colnames(Tany12)), invert=T) #CheckInput = Tany07Clean #CheckUMAP(Tany_Seu) Tany01_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > 0 & Tany_UMAP$UMAP_2 > 30 | row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -20 & Tany_UMAP$UMAP_1 < 0 & Tany_UMAP$UMAP_2 > 26.5 | row.names(Tany_UMAP) %in% row.names(Tany01Clean)) Tany04_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -23 & Tany_UMAP$UMAP_2 > 5 & Tany_UMAP$UMAP_2 < 25 | row.names(Tany_UMAP) %in% row.names(Tany04Clean)) Tany06_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -25 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > -40 & Tany_UMAP$UMAP_2 < -5| row.names(Tany_UMAP) %in% row.names(Tany06Clean)) Tany09_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -12 & Tany_UMAP$UMAP_1 < 5 & Tany_UMAP$UMAP_2 < -30 & ! row.names(Tany_UMAP) %in% row.names(Tany06_Extras) | row.names(Tany_UMAP) %in% row.names(Tany09Clean)) Tany10_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -20 & Tany_UMAP$UMAP_2 > -12 & Tany_UMAP$UMAP_2 < 5 & ! row.names(Tany_UMAP) %in% c(row.names(Tany01_Extras), row.names(Tany04_Extras), row.names(Tany06_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany10Clean)) Tany11_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -25 & Tany_UMAP$UMAP_2 > -25 & Tany_UMAP$UMAP_2 < -12 | row.names(Tany_UMAP) %in% row.names(Tany11Clean)) Tany12_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 < -25 & Tany_UMAP$UMAP_2 > 25 | row.names(Tany_UMAP) %in% colnames(Tany12)) Tany02_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -27 & Tany_UMAP$UMAP_1 < 2 & Tany_UMAP$UMAP_2 > -15 & Tany_UMAP$UMAP_2 < 16 & ! row.names(Tany_UMAP) %in% c(row.names(Tany10_Extras), row.names(Tany04_Extras), row.names(Tany06_Extras), row.names(Tany11_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany02Clean)) Tany08_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -24 & Tany_UMAP$UMAP_1 < -5 & Tany_UMAP$UMAP_2 > 5 & Tany_UMAP$UMAP_2 < 27 & ! row.names(Tany_UMAP) %in% c(row.names(Tany01_Extras), row.names(Tany02_Extras) ) | row.names(Tany_UMAP) %in% row.names(Tany08Clean)) Tany03_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -3 & Tany_UMAP$UMAP_1 < 33 & Tany_UMAP$UMAP_2 > -10 & Tany_UMAP$UMAP_2 < 30 & ! row.names(Tany_UMAP) %in% c(row.names(Tany01_Extras), row.names(Tany02_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany03Clean)) Tany05_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > 18 & Tany_UMAP$UMAP_2 > -22 & Tany_UMAP$UMAP_2 < 10 & ! row.names(Tany_UMAP) %in% row.names(Tany03_Extras) | row.names(Tany_UMAP) %in% row.names(Tany05Clean)) Tany07_Extras = subset(Tany_UMAP, row.names(Tany_UMAP) %in% colnames(Tany_REST) & Tany_UMAP$UMAP_1 > -5 & Tany_UMAP$UMAP_1 < 25 & Tany_UMAP$UMAP_2 > -31 & Tany_UMAP$UMAP_2 < -10 & ! row.names(Tany_UMAP) %in% c(row.names(Tany03_Extras), row.names(Tany02_Extras), row.names(Tany05_Extras)) | row.names(Tany_UMAP) %in% row.names(Tany07Clean)) #& ! row.names(Tany_UMAP) %in% c(row.names(Tany02_Extras)) , row.names(?) #CheckInput = Tany06_Extras #CheckUMAP(Tany_Seu) Tany_Assigns = GenerateMetaData_Barcodes(list("Alpha_01" = Tany01_Extras, "Alpha_02" = Tany02_Extras, "Alpha_03" = Tany03_Extras, "Beta_01" = Tany04_Extras, "Beta_02" = Tany05_Extras, "RG_16" = Tany06_Extras, "Alpha_04" = Tany07_Extras, "Tany_07" = Tany08_Extras, "RG_17" = Tany09_Extras, "Tany_08" = Tany10_Extras, "Alpha_05" = Tany11_Extras, "Alpha_05" = Tany12_Extras)) #Tany02_Extras_Pt2 = subset(Tany_Seu, cells = Tany_Assigns$Barcodes, invert=T) CheckInput = Tany02_Extras_Pt2 CheckUMAP(Tany_Seu) Tany_Assigns$Dups = duplicated(Tany_Assigns$Barcodes) | duplicated(Tany_Assigns$Barcodes, fromLast=T) Tany_Assigns_T = subset(Tany_Assigns, Tany_Assigns$Dups == T) unique(Tany_Assigns_T$Pop) Tany_Assigns = GenerateMetaData(list("Alpha_01" = Tany01_Extras, "Alpha_02" = Tany02_Extras, "Alpha_03" = Tany03_Extras, "Beta_01" = Tany04_Extras, "Beta_02" = Tany05_Extras, "RG_16" = Tany06_Extras, "Alpha_04" = Tany07_Extras, "Tany_07" = Tany08_Extras, "RG_17" = Tany09_Extras, "Tany_08" = Tany10_Extras, "Alpha_05" = Tany11_Extras, "Alpha_05" = Tany12_Extras, "Alpha_02" =Tany02_Extras_Pt2)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Ependymocytes
Idents(KaZhouAll) = "MainClusters" Ependy_Seu = subset(KaZhouAll, idents = "Ependymal") DefaultAssay(Ependy_Seu) = "integrated" Ependy_Seu = FindVariableFeatures(Ependy_Seu) Ependy_Seu = ScaleData(Ependy_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Ependy_Seu) = "integrated" Ependy_Seu = RunPCA(Ependy_Seu, npcs = 10) #Ependy_Seu <- RunHarmony(Ependy_Seu, group.by.vars = "Timepoint2") Ependy_Seu <- RunUMAP(Ependy_Seu, dims = 1:10, spread= 2)#, reduction = "harmony") DefaultAssay(Ependy_Seu) = "RNA" ##### set.dim = c(10) set.res = c(1) set.kparam = c(100) ClusterFunc_All_RNA(Ependy_Seu) DefaultAssay(Ependy_Seu) = "integrated" Ependy_Seu <- FindNeighbors(Ependy_Seu, k.param=100, dims=1:10) Ependy_Seu <- FindClusters(Ependy_Seu, resolution = 1) DefaultAssay(Ependy_Seu) = "RNA" CheckInput = Ependy01_Extras CheckUMAP(Ependy_Seu) Ependy_UMAP = as.data.frame(Ependy_Seu@reductions$umap@cell.embeddings) Ependy01 = subset(Ependy_Seu, idents = c(5), invert=T) Ependy01Clean = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy01) & Ependy_UMAP$UMAP_1 < 10) Ependy02 = subset(Ependy_Seu, idents = c(5)) Ependy02Clean = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy02) & Ependy_UMAP$UMAP_1 > 10) Ependy_REST = subset(Ependy_Seu, cells = c(row.names(Ependy01Clean), row.names(Ependy02Clean)), invert=T) Ependy01_Extras = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy_REST) & Ependy_UMAP$UMAP_1 < 10 | row.names(Ependy_UMAP) %in% row.names(Ependy01Clean)) Ependy02_Extras = subset(Ependy_UMAP, row.names(Ependy_UMAP) %in% colnames(Ependy_REST) & Ependy_UMAP$UMAP_1 > 10 | row.names(Ependy_UMAP) %in% row.names(Ependy02Clean)) Ependy_Assigns = GenerateMetaData(list( "Ependy_01" = Ependy01_Extras, "Ependy_02" = Ependy02_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
Oligodendrocytes
Idents(KaZhouAll) = "MainClusters" Olig_Seu = subset(KaZhouAll, idents = c("Oligodendrocytes")) #Olig_Assigns= subset(MainClusters, MainClusters$Pop %in% c("Oligodendrocytes [Dividing]", "Oligodendrocytes [Immature]", "Oligodendrocytes [Intermediate]", "Oligodendrocytes [Mature]")) DefaultAssay(Olig_Seu) = "integrated" Olig_Seu = FindVariableFeatures(Olig_Seu) Olig_Seu = ScaleData(Olig_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(Olig_Seu) = "integrated" Olig_Seu = RunPCA(Olig_Seu, npcs = 20) Olig_Seu <- RunHarmony(Olig_Seu, group.by.vars = "Timepoint2") Olig_Seu <- RunUMAP(Olig_Seu, dims = 1:20, spread= 2, reduction = "harmony") DefaultAssay(Olig_Seu) = "RNA" set.dim = c(20) set.res = c(1) set.kparam = c(20) ClusterFunc_All_RNA(Olig_Seu) DefaultAssay(Olig_Seu) = "integrated" Olig_Seu <- FindNeighbors(Olig_Seu, k.param=20, dims=1:20) Olig_Seu <- FindClusters(Olig_Seu, resolution = 1) DefaultAssay(Olig_Seu) = "RNA" CheckInput = Olig07Clean CheckUMAP(Olig_Seu) Olig_UMAP = as.data.frame(Olig_Seu@reductions$umap@cell.embeddings) Olig01 = subset(Olig_Seu, idents = c(9)) #Dividing Olig02 = subset(Olig_Seu, idents = c(13)) #Dividing Olig02Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig02) & Olig_UMAP$UMAP_1 > 0 & Olig_UMAP$UMAP_1 < 5 & Olig_UMAP$UMAP_2 > 1) Olig03 = subset(Olig_Seu, idents = c(0,1,5,6,8)) #Immature Olig03Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig03) & Olig_UMAP$UMAP_1 > 0 & Olig_UMAP$UMAP_2 > -10 & Olig_UMAP$UMAP_2 < 2 | row.names(Olig_UMAP) %in% colnames(Olig03) & Olig_UMAP$UMAP_1 > -7 & Olig_UMAP$UMAP_2 > -7 & Olig_UMAP$UMAP_2 < -0.3) Olig04 = subset(Olig_Seu, idents = c(2))#Immature Olig04Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig04) & Olig_UMAP$UMAP_1 > 6 & Olig_UMAP$UMAP_2 > 1 & Olig_UMAP$UMAP_2 < 12) Olig05 = subset(Olig_Seu, idents = c(10)) #Immature Olig05Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig05) & Olig_UMAP$UMAP_1 > -4 & Olig_UMAP$UMAP_1 < 3 & Olig_UMAP$UMAP_2 > 0 & Olig_UMAP$UMAP_2 < 5 ) Olig06 = subset(Olig_Seu, idents = c(11)) #Immature Olig06Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig06) & Olig_UMAP$UMAP_1 > -6 & Olig_UMAP$UMAP_1 < -1 & Olig_UMAP$UMAP_2 > -1 & Olig_UMAP$UMAP_2 < 3) Olig07 = subset(Olig_Seu, idents = c(16)) #Immature Olig07Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig07) & Olig_UMAP$UMAP_1 > -4 & Olig_UMAP$UMAP_1 < 3 & Olig_UMAP$UMAP_2> 9) Olig08 = subset(Olig_Seu, idents = c(17)) #Immature Olig08Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig08) & Olig_UMAP$UMAP_2 > 6 & Olig_UMAP$UMAP_2 < 9) Olig09 = subset(Olig_Seu, idents = c(14)) #Immature Olig10 = subset(Olig_Seu, idents = c(7,12)) #Intermediate Olig10Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig10) & Olig_UMAP$UMAP_1 > -12& Olig_UMAP$UMAP_1 < -3 & Olig_UMAP$UMAP_2 < 2.5) Olig11 = subset(Olig_Seu, idents = c(3,4,15)) #Mature Olig11Clean = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig11) & Olig_UMAP$UMAP_1 < -8) Olig_REST = subset(Olig_Seu, cells = c(colnames(Olig01), row.names(Olig02Clean), row.names(Olig03Clean), row.names(Olig04Clean), row.names(Olig05Clean), row.names(Olig06Clean), row.names(Olig07Clean), row.names(Olig08Clean),colnames(Olig09), row.names(Olig10Clean),row.names(Olig11Clean)), invert=T) CheckInput = Olig03_Extras CheckUMAP(Olig_Seu) Olig01_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 5 & Olig_UMAP$UMAP_2 > 10.5 | row.names(Olig_UMAP) %in% colnames(Olig01)) Olig02_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 2 & Olig_UMAP$UMAP_1 < 5 & Olig_UMAP$UMAP_2 > 2 | row.names(Olig_UMAP) %in% row.names(Olig02Clean)) Olig03_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 0 & Olig_UMAP$UMAP_2 > -10 & Olig_UMAP$UMAP_2 < 2 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -7 & Olig_UMAP$UMAP_2 > -7 & Olig_UMAP$UMAP_2 < -0.3 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -7 & Olig_UMAP$UMAP_2 > -10 & Olig_UMAP$UMAP_2 < -5 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 5 & Olig_UMAP$UMAP_2 < 3 | row.names(Olig_UMAP) %in% row.names(Olig03Clean)) Olig04_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > 5 & Olig_UMAP$UMAP_2 > 3 & Olig_UMAP$UMAP_2 < 10.5| row.names(Olig_UMAP) %in% row.names(Olig04Clean)) Olig05_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST)& Olig_UMAP$UMAP_1 > -4 & Olig_UMAP$UMAP_1 < 3 & Olig_UMAP$UMAP_2 > 2 & Olig_UMAP$UMAP_2 < 5.5 | row.names(Olig_UMAP) %in% row.names(Olig05Clean)) Olig06_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST)& Olig_UMAP$UMAP_1 > -6 & Olig_UMAP$UMAP_1 < 0 & Olig_UMAP$UMAP_2 > -1 & Olig_UMAP$UMAP_2 < 3 & ! row.names(Olig_UMAP) %in% c(row.names(Olig05_Extras), row.names(Olig03_Extras)) | row.names(Olig_UMAP) %in% row.names(Olig06Clean)) ## Olig07_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -3 & Olig_UMAP$UMAP_1 < 2 & Olig_UMAP$UMAP_2 > 7 | row.names(Olig_UMAP) %in% row.names(Olig07Clean)) Olig08_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -6 & Olig_UMAP$UMAP_1 < -3 & Olig_UMAP$UMAP_2 > 4 & ! row.names(Olig_UMAP) %in% row.names(Olig05_Extras) | row.names(Olig_UMAP) %in% row.names(Olig08Clean)) Olig09_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST)& Olig_UMAP$UMAP_2 < -12 | row.names(Olig_UMAP) %in% colnames(Olig09)) Olig11_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 < -11 & Olig_UMAP$UMAP_2 > -3 | row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 < -9 & Olig_UMAP$UMAP_2 > 3 | row.names(Olig_UMAP) %in% row.names(Olig11Clean)) Olig10_Extras = subset(Olig_UMAP, row.names(Olig_UMAP) %in% colnames(Olig_REST) & Olig_UMAP$UMAP_1 > -11 & Olig_UMAP$UMAP_1 < -3 & Olig_UMAP$UMAP_2 < 4 & ! row.names(Olig_UMAP) %in% c(row.names(Olig11_Extras), row.names(Olig03_Extras), row.names(Olig06_Extras)) | row.names(Olig_UMAP) %in% row.names(Olig10Clean)) CheckInput = Olig03_Extras CheckUMAP(Olig_Seu) Olig_Assigns = GenerateMetaData_Barcodes(list("Dividing_01" = Olig01_Extras, "Dividing_02" = Olig02_Extras, "Immature_01" = Olig03_Extras, "Immature_02" = Olig04_Extras, "Immature_03" = Olig05_Extras, "Immature_04" = Olig06_Extras, "Immature_05" = Olig07_Extras, "Immature_06" = Olig08_Extras, "Immature_07" = Olig09_Extras, "Intermediate" = Olig10_Extras, "Mature" = Olig11_Extras)) Olig02_Extras_Pt2 = subset(Olig_Seu, cells = Olig_Assigns$Barcodes, invert=T) CheckInput = Olig02_Extras_Pt2 CheckUMAP(Olig_Seu) Olig_Assigns$Dups = duplicated(Olig_Assigns$Barcodes) | duplicated(Olig_Assigns$Barcodes, fromLast=T) Olig_Assigns_T = subset(Olig_Assigns, Olig_Assigns$Dups == T) unique(Olig_Assigns_T$Pop) Olig_Assigns = GenerateMetaData(list("Dividing_01" = Olig01_Extras, "Dividing_02" = Olig02_Extras, "Immature_01" = Olig03_Extras, "Immature_02" = Olig04_Extras, "Immature_03" = Olig05_Extras, "Immature_04" = Olig06_Extras, "Immature_05" = Olig07_Extras, "Immature_06" = Olig08_Extras, "Immature_07" = Olig09_Extras, "Intermediate" = Olig10_Extras, "Mature" = Olig11_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Assigns", "Olig_Seu"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
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Neuronal subclustering:
Read in NEURONAL data and iterate UMAPs
EdKaZhouHypoNeurons = readRDS("~/Downloads/EdKaZhouHypoNeurons_mt10_integrated.rds") set.seed(100) EdKaZhouHypoNeurons@meta.data$SampleBroad = gsub("_.*", "", gsub("22T", "22", gsub("CS22", "GW10", EdKaZhouHypoNeurons@meta.data$sample))) EdKaZhouHypoNeurons@meta.data$SampleBroad = ifelse(EdKaZhouHypoNeurons@meta.data$SampleBroad %in% c("10X190", "10X192", "10X193", "10X203", "10X360", "10X362", "10X376", "10X380", "10X389", "10X392"), EdKaZhouHypoNeurons@meta.data$Roi, EdKaZhouHypoNeurons@meta.data$SampleBroad) EdKaZhouHypoNeurons@meta.data$SampleBroad = gsub("\\<Human HTHma\\>", "Adult_MN", gsub("\\<Human HTHma-HTHtub\\>", "Adult_TUB/MN", gsub("\\<Human HTHpo\\>", "Adult_PO", gsub("\\<Human HTHpo-HTHso\\>", "Adult_PO/SO", gsub("\\<Human HTHso\\>", "Adult_SO", gsub("\\<Human HTHso-HTHtub\\>", "Adult_SO/TUB", gsub("\\<Human HTHtub\\>", "Adult_TUB", gsub("\\<Human MN\\>", "Adult_MN", EdKaZhouHypoNeurons@meta.data$SampleBroad)))))))) EdKaZhouHypoNeurons@meta.data$SampleAdult = gsub("_.*", "", EdKaZhouHypoNeurons@meta.data$SampleBroad) MainAssign = read.csv("~/Dropbox/LabMac/Fig2_Trajectories_GLOTMP_17JAN_20_20_3_k100AssignsByBarc.csv", na.strings=c("","Unassigned")) #, "ARC", "TM", "DMH", "AH", "SCN", "PVH", "SMN", "VMH", "PO", "LH" for(Pops in c("NA", "MN")){ GetBarcs = subset(MainAssign, MainAssign$Nuclei == Pops) SubsetSeu = subset(EdKaZhouHypoNeurons, cells = GetBarcs$Barcs) DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = FindVariableFeatures(SubsetSeu) SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DPlist2 = list() DPlist = list() FPlist = list() Filename = paste(Pops, "28MAR23", sep="") for(y in seq(10,50,10)){ DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = RunPCA(SubsetSeu, npcs = y) for(z in seq(10,y,10)){ for(s in c(2,5,10)){ SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s) DefaultAssay(SubsetSeu) = "RNA" FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, CompileNeurons, reduction="umap") DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "SampleAdult") + labs(title = paste("PCA", y, "_dims", z, "_spread", s)) DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "SampleAdult", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s)) }}} pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=75/1.3) print(FPlist) dev.off() pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5) print(DPlist) dev.off() pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5) print(DPlist2) dev.off() } for(Pops in c("VMH", "LH")){ GetBarcs = subset(MainAssign, MainAssign$Nuclei == Pops) SubsetSeu = subset(EdKaZhouHypoNeurons, cells = GetBarcs$Barcs) DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = FindVariableFeatures(SubsetSeu) SubsetSeu = ScaleData(SubsetSeu, vars.to.regress = c( "percent.mt", "nFeature_RNA"), verbose = F) DPlist2 = list() DPlist = list() FPlist = list() Filename = paste(Pops, "28MAR23_Harmony", sep="") for(y in seq(10,50,10)){ DefaultAssay(SubsetSeu) = "integrated" SubsetSeu = RunPCA(SubsetSeu, npcs = y) SubsetSeu <- RunHarmony(SubsetSeu, group.by.vars = "SampleAdult") for(z in seq(10,y,10)){ for(s in c(2,5,10)){ SubsetSeu <- RunUMAP(SubsetSeu, dims = 1:z, spread= s, reduction="harmony") DefaultAssay(SubsetSeu) = "RNA" FPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = FeaturePlot(SubsetSeu, CompileNeurons, reduction="umap") DPlist[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", split.by = "SampleAdult") + labs(title = paste("PCA", y, "_dims", z, "_spread", s)) DPlist2[[paste("PCA", y, "_dims", z, "_spread", s)]] = DimPlot(SubsetSeu, reduction="umap", group.by = "SampleAdult", label=T) + labs(title = paste("PCA", y, "dims", z, "spread", s)) }}} pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_FeaturePlots.pdf", sep=""), width=20, height=75/1.3) print(FPlist) dev.off() pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_SPLITUMAP.pdf", sep=""), width=50, height=5) print(DPlist) dev.off() pdf(paste("FETAL_HYPO_NEURONS_", Filename, "_GROUPUMAP.pdf", sep=""), width=10, height=5) print(DPlist2) dev.off() }
ARC
GetARCBarcs = subset(MainAssign, MainAssign$Nuclei == "ARC") ARC_Seu = subset(EdKaZhouHypoNeurons, cells = GetARCBarcs$Barcs) DefaultAssay(ARC_Seu) = "integrated" ARC_Seu = FindVariableFeatures(ARC_Seu) ARC_Seu = ScaleData(ARC_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(ARC_Seu) = "integrated" ARC_Seu = RunPCA(ARC_Seu, npcs = 20) ARC_Seu <- RunUMAP(ARC_Seu, dims = 1:20, spread= 2) DefaultAssay(ARC_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(50) ##ClusterFunc_All_RNA(ARC_Seu) DefaultAssay(ARC_Seu) = "integrated" ARC_Seu <- FindNeighbors(ARC_Seu, k.param=50, dims=1:20) ARC_Seu <- FindClusters(ARC_Seu, resolution = 1) DefaultAssay(ARC_Seu) = "RNA" ARC_UMAP = as.data.frame(ARC_Seu@reductions$umap@cell.embeddings) ARC01 = subset(ARC_Seu, idents = 0) ARC01Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC01) & ARC_UMAP$UMAP_1 < -3 & ARC_UMAP$UMAP_2 > 5) ARC02 = subset(ARC_Seu, idents = 1) ARC02Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC02) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_2 > -9 & ARC_UMAP$UMAP_2 < 4) TM01 = subset(ARC_Seu, idents = c(2,3)) TM01Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(TM01) & ARC_UMAP$UMAP_1 > -3 & ARC_UMAP$UMAP_2 > 7) ARC03 = subset(ARC_Seu, idents = 4) ARC03Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC03) & ARC_UMAP$UMAP_1 < 2 & ARC_UMAP$UMAP_2 < -11) ARC04 = subset(ARC_Seu, idents = 5) ARC04Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC04) & ARC_UMAP$UMAP_1 < -7 & ARC_UMAP$UMAP_2 < 5) ARC05 = subset(ARC_Seu, idents = 6) ARC06 = subset(ARC_Seu, idents = 7) ARC06Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC06) & ARC_UMAP$UMAP_1 > 10 & ARC_UMAP$UMAP_2 < -0.5) ARC07 = subset(ARC_Seu, idents = c(8, 18)) ARC07Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC07) & ARC_UMAP$UMAP_1 < 0 & ARC_UMAP$UMAP_2 < 8) ARC08 = subset(ARC_Seu, idents = 9) ARC08Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC08) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_2 < -5) ARC09 = subset(ARC_Seu, idents = 10) ARC09Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC09) & ARC_UMAP$UMAP_1 > 0) ARC10 = subset(ARC_Seu, idents = 11) ARC11 = subset(ARC_Seu, idents = 12) ARC11Clean = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC11) & ARC_UMAP$UMAP_1 > -2 & ARC_UMAP$UMAP_1 < 3) ARC12 = subset(ARC_Seu, idents = 13) ARC13 = subset(ARC_Seu, idents = 14) ARC14 = subset(ARC_Seu, idents = 15) ARC15 = subset(ARC_Seu, idents = 16) ARC16 = subset(ARC_Seu, idents = 17) ARC17 = subset(ARC_Seu, idents = 19) ARC_REST = subset(ARC_Seu, cells = c(colnames(ARC05), colnames(ARC10), colnames(ARC12), colnames(ARC13), colnames(ARC14), colnames(ARC15), colnames(ARC16), colnames(ARC17), row.names(ARC01Clean), row.names(ARC02Clean), row.names(ARC03Clean), row.names(ARC04Clean), row.names(ARC06Clean), row.names(ARC07Clean), row.names(ARC08Clean),row.names(ARC09Clean), row.names(ARC11Clean), row.names(TM01Clean)), invert=T) #CheckInput = ARC_REST #CheckUMAP(ARC_Seu) ARC01_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < -5 & ARC_UMAP$UMAP_2 > 4 | row.names(ARC_UMAP) %in% row.names(ARC01Clean)) ARC02_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 4 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 > -3 & ARC_UMAP$UMAP_2 < 4 | row.names(ARC_UMAP) %in% row.names(ARC02Clean)) TM01_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > -3 & ARC_UMAP$UMAP_2 > 8 | row.names(ARC_UMAP) %in% row.names(TM01Clean)) ARC03_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < 0 & ARC_UMAP$UMAP_2 < -11 | row.names(ARC_UMAP) %in% row.names(ARC03Clean)) ARC04_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < -10 & ARC_UMAP$UMAP_2 < 5 & ! row.names(ARC_UMAP) %in% row.names(ARC01_Extras) | row.names(ARC_UMAP) %in% row.names(ARC04Clean)) ARC05_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_2 < -13 | row.names(ARC_UMAP) %in% colnames(ARC05)) #ARC06_Extras #NONE ARC08_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% row.names(ARC05_Extras) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_1 < 4 & ARC_UMAP$UMAP_2 < -6 | row.names(ARC_UMAP) %in% row.names(ARC08Clean)) ARC09_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC02_Extras), row.names(ARC08_Extras)) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 > -10 & ARC_UMAP$UMAP_2 < -1 | row.names(ARC_UMAP) %in% row.names(ARC09Clean)) ARC10_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 < -10 & ARC_UMAP$UMAP_2 < -4 & ! row.names(ARC_UMAP) %in% row.names(ARC04_Extras) | row.names(ARC_UMAP) %in% colnames(ARC10)) ARC11_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > -2 & ARC_UMAP$UMAP_1 < 2 & ARC_UMAP$UMAP_2 > -2.5 & ARC_UMAP$UMAP_2 < 2 | row.names(ARC_UMAP) %in% row.names(ARC11Clean)) #ARC12_Extras #NONE ARC13_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC08_Extras),row.names(ARC09_Extras), row.names(ARC05_Extras)) & ARC_UMAP$UMAP_1 > 3 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 < -6 | row.names(ARC_UMAP) %in% colnames(ARC13)) ARC14_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > -1 & ARC_UMAP$UMAP_1 < 5 & ARC_UMAP$UMAP_2 > 0 & ARC_UMAP$UMAP_2 < 9 & ! row.names(ARC_UMAP) %in% c(row.names(TM01_Extras), row.names(ARC11_Extras)) | row.names(ARC_UMAP) %in% colnames(ARC14)) ARC15_Extras = subset(ARC_UMAP,row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 5 & ARC_UMAP$UMAP_1 < 10 & ARC_UMAP$UMAP_2 > 2 & ARC_UMAP$UMAP_2 < 10 & ! row.names(ARC_UMAP) %in% c(row.names(TM01_Extras), row.names(ARC02_Extras)) | row.names(ARC_UMAP) %in% colnames(ARC15)) ARC16_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ARC_UMAP$UMAP_1 > 10 | row.names(ARC_UMAP) %in% colnames(ARC16)) ARC17_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC08_Extras),row.names(ARC09_Extras), row.names(ARC05_Extras)) & ARC_UMAP$UMAP_1 > 0 & ARC_UMAP$UMAP_1 < 3 & ARC_UMAP$UMAP_2 > -8 & ARC_UMAP$UMAP_2 < -4 | row.names(ARC_UMAP) %in% colnames(ARC17)) ARC07_Extras = subset(ARC_UMAP, row.names(ARC_UMAP) %in% colnames(ARC_REST) & ! row.names(ARC_UMAP) %in% c(row.names(ARC01_Extras), row.names(ARC11_Extras), row.names(TM01_Extras)) & ARC_UMAP$UMAP_1 > -10 & ARC_UMAP$UMAP_1 < 0 & ARC_UMAP$UMAP_2 > -5 | row.names(ARC_UMAP) %in% row.names(ARC07Clean)) #CheckInput = ARC10_Extras #CheckUMAP(ARC_Seu) set.dim = 20 set.res = 1 set.kparam = c(20) #ClusterFunc_All_RNA(ARC10_Seu) ARC07_Seu = subset(ARC_Seu, cells = row.names(ARC07_Extras)) #ClusterFunc_All_RNA(ARC07_Seu) ARC10_Seu = subset(ARC_Seu, cells = row.names(ARC10_Extras)) #ClusterFunc_All_RNA(ARC10_Seu) ARC14_Seu = subset(ARC_Seu, cells = row.names(ARC14_Extras)) #ClusterFunc_All_RNA(ARC14_Seu) DefaultAssay(ARC07_Seu) = "integrated" ARC07_Seu <- FindNeighbors(ARC07_Seu, k.param=5, dims=1:20) ARC07_Seu <- FindClusters(ARC07_Seu, resolution = 1) ARC_18 = subset(ARC07_Seu, idents = c(3,7)) ARC_19 = subset(ARC07_Seu, idents = c(0,2)) ARC_07_New = subset(ARC07_Seu, idents = c(3,7,0,2), invert=T) DefaultAssay(ARC10_Seu) = "integrated" ARC10_Seu <- FindNeighbors(ARC10_Seu, k.param=10, dims=1:20) ARC10_Seu <- FindClusters(ARC10_Seu, resolution = 1) ARC_20 = subset(ARC10_Seu, idents = c(1,3)) ARC_10_New = subset(ARC10_Seu, idents = c(1,3), invert=T) DefaultAssay(ARC14_Seu) = "integrated" ARC14_Seu <- FindNeighbors(ARC14_Seu, k.param=20, dims=1:20) ARC14_Seu <- FindClusters(ARC14_Seu, resolution = 1) ARC_21 = subset(ARC14_Seu, idents = c(0,1,5)) ARC_14_New = subset(ARC14_Seu, idents = c(0,1,5), invert=T) ### ARC_REST = subset(ARC_Seu, cells = c(colnames(ARC05), colnames(ARC10), colnames(ARC12), colnames(ARC13), colnames(ARC14), colnames(ARC15), colnames(ARC16), colnames(ARC17), row.names(ARC01Clean), row.names(ARC02Clean), row.names(ARC03Clean), row.names(ARC04Clean), row.names(ARC06Clean), row.names(ARC07Clean), row.names(ARC08Clean),row.names(ARC09Clean), row.names(ARC11Clean), row.names(TM01Clean)), invert=T) ARC_Assigns = GenerateMetaData(list("ARC_01" = ARC01_Extras, "ARC_02" = ARC02_Extras, "ARC_03" = ARC03_Extras, "ARC_04" = ARC04_Extras, "ARC_05" = ARC05_Extras, "ARC_07" = ARC_07_New,"ARC_08" = ARC08_Extras, "ARC_09" = ARC09_Extras, "ARC_10" = ARC_10_New, "ARC_11" = ARC11_Extras,"ARC_13" = ARC13_Extras,"ARC_14" = ARC_14_New,"ARC_15" = ARC15_Extras, "ARC_16" = ARC16_Extras,"ARC_17" = ARC17_Extras,"TM_01" = TM01_Extras, "ARC_06" = ARC06Clean,"ARC_12" = ARC12, "ARC_18" = ARC_18, "ARC_19" = ARC_19, "ARC_20" = ARC_20, "ARC_21" = ARC_21)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
PVH
GetPVHBarcs = subset(MainAssign, MainAssign$Nuclei == "PVH") PVH_Seu = subset(EdKaZhouHypoNeurons, cells = GetPVHBarcs$Barcs) DefaultAssay(PVH_Seu) = "integrated" PVH_Seu = FindVariableFeatures(PVH_Seu) PVH_Seu = ScaleData(PVH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(PVH_Seu) = "integrated" PVH_Seu = RunPCA(PVH_Seu, npcs = 20) PVH_Seu <- RunUMAP(PVH_Seu, dims = 1:20, spread= 5) DefaultAssay(PVH_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(50) ##ClusterFunc_All_RNA(PVH_Seu) DefaultAssay(PVH_Seu) = "integrated" PVH_Seu <- FindNeighbors(PVH_Seu, k.param=50, dims=1:20) PVH_Seu <- FindClusters(PVH_Seu, resolution = 1) DefaultAssay(PVH_Seu) = "RNA" PVH_UMAP = as.data.frame(PVH_Seu@reductions$umap@cell.embeddings) PVH01 = subset(PVH_Seu, idents = 0) PVH01Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH01) & PVH_UMAP$UMAP_1 > -12.5 & PVH_UMAP$UMAP_1 < 4 & PVH_UMAP$UMAP_2 > -10 & PVH_UMAP$UMAP_2 < 8) PVH02 = subset(PVH_Seu, idents = 1) PVH02Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH02) & PVH_UMAP$UMAP_1 > -10 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 12) PVH03 = subset(PVH_Seu, idents = 2) PVH03Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH03) & PVH_UMAP$UMAP_1 > 10 & PVH_UMAP$UMAP_2 > 10) PVH04 = subset(PVH_Seu, idents = 3) PVH04Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH04) & PVH_UMAP$UMAP_1 < -20) PVH05 = subset(PVH_Seu, idents = 4) PVH05Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH05) & PVH_UMAP$UMAP_1 < 25 & PVH_UMAP$UMAP_1 > 13 & PVH_UMAP$UMAP_2 < -10) PVH06 = subset(PVH_Seu, idents = 5) PVH06Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH06) & PVH_UMAP$UMAP_1 < 3 & PVH_UMAP$UMAP_1 > -10 & PVH_UMAP$UMAP_2 < 3 & PVH_UMAP$UMAP_2 > -10) PVH07 = subset(PVH_Seu, idents = 6) PVH08 = subset(PVH_Seu, idents = 7) PVH09 = subset(PVH_Seu, idents = 8) PVH09Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH09) & PVH_UMAP$UMAP_1 < -10 & PVH_UMAP$UMAP_1 > -20 & PVH_UMAP$UMAP_2 > -10) PVH10 = subset(PVH_Seu, idents = 9) PVH10Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH10) & PVH_UMAP$UMAP_2 < 10) PVH11 = subset(PVH_Seu, idents = 10) PVH11Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH11) & PVH_UMAP$UMAP_1 > 20) PVH12 = subset(PVH_Seu, idents = 11) PVH12Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH12) & PVH_UMAP$UMAP_1 < 10) PVH13 = subset(PVH_Seu, idents = 12) PVH14 = subset(PVH_Seu, idents = 13) PVH14Clean = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH14) & PVH_UMAP$UMAP_2 > -20) PVH15 = subset(PVH_Seu, idents = 14) PVH16 = subset(PVH_Seu, idents = c(15, 16)) #CheckInput = PVH17 #CheckUMAP(PVH_Seu) PVH_REST = subset(PVH_Seu, cells = c(colnames(PVH07), colnames(PVH08), colnames(PVH13), colnames(PVH15), colnames(PVH16), row.names(PVH01Clean), row.names(PVH02Clean), row.names(PVH03Clean), row.names(PVH04Clean), row.names(PVH05Clean),row.names(PVH06Clean), row.names(PVH09Clean),row.names(PVH10Clean), row.names(PVH11Clean), row.names(PVH12Clean), row.names(PVH14Clean)), invert=T) #CheckInput = PVH_REST #CheckUMAP(PVH_Seu) PVH01_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > -12.5 & PVH_UMAP$UMAP_1 < 0 & PVH_UMAP$UMAP_2 > -10 & PVH_UMAP$UMAP_2 < 8 | row.names(PVH_UMAP) %in% row.names(PVH01Clean)) PVH02_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > -3 & PVH_UMAP$UMAP_1 < 10 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 14 & ! row.names(PVH_UMAP) %in% row.names(PVH01_Extras) | row.names(PVH_UMAP) %in% row.names(PVH02Clean) | row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > -3 & PVH_UMAP$UMAP_1 < 18 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 12 & ! row.names(PVH_UMAP) %in% row.names(PVH01_Extras)| row.names(PVH_UMAP) %in% row.names(PVH02Clean)) PVH03_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > 10 & PVH_UMAP$UMAP_2 > 10| row.names(PVH_UMAP) %in% row.names(PVH03Clean)) PVH04_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -22 & PVH_UMAP$UMAP_2 > -5 & PVH_UMAP$UMAP_2 < 12| row.names(PVH_UMAP) %in% row.names(PVH04Clean)) PVH05_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 25 & PVH_UMAP$UMAP_1 > 13 & PVH_UMAP$UMAP_2 < -10| row.names(PVH_UMAP) %in% row.names(PVH05Clean)) #PVH06_Extras #None PVH07_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 0 & PVH_UMAP$UMAP_1 > -15 & PVH_UMAP$UMAP_2 < -10| row.names(PVH_UMAP) %in% colnames(PVH07)) PVH08_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > 20 & PVH_UMAP$UMAP_2 < -10 & ! row.names(PVH_UMAP) %in% row.names(PVH05_Extras) | row.names(PVH_UMAP) %in% colnames(PVH08)) PVH09_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -10 & PVH_UMAP$UMAP_1 > -22 & PVH_UMAP$UMAP_2 < 5 & PVH_UMAP$UMAP_2 > -5| row.names(PVH_UMAP) %in% row.names(PVH09Clean)) PVH10_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_2 > -10 & PVH_UMAP$UMAP_2 < 0 & PVH_UMAP$UMAP_1 > 0 & PVH_UMAP$UMAP_1 < 10| row.names(PVH_UMAP) %in% row.names(PVH10Clean)) PVH11_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 > 20 & PVH_UMAP$UMAP_2 > 0 & PVH_UMAP$UMAP_2 < 10| row.names(PVH_UMAP) %in% row.names(PVH11Clean)) PVH12_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 15 & PVH_UMAP$UMAP_1 > 0 & PVH_UMAP$UMAP_2 > 15| row.names(PVH_UMAP) %in% row.names(PVH12Clean)) PVH13_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < 18 & PVH_UMAP$UMAP_1 > 0 & PVH_UMAP$UMAP_2 < -15 & ! row.names(PVH_UMAP) %in% c(row.names(PVH05_Extras), row.names(PVH08_Extras)) | row.names(PVH_UMAP) %in% colnames(PVH13)) PVH14_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -15 & PVH_UMAP$UMAP_2 > -20 & PVH_UMAP$UMAP_2 < -5| row.names(PVH_UMAP) %in% row.names(PVH14Clean)) PVH15_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & PVH_UMAP$UMAP_1 < -15 & PVH_UMAP$UMAP_2 < -20| row.names(PVH_UMAP) %in% colnames(PVH15)) PVH16_Extras = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH_REST) & ! row.names(PVH_UMAP) %in% c(row.names(PVH01_Extras), row.names(PVH02_Extras), row.names(PVH03_Extras), row.names(PVH04_Extras), row.names(PVH05_Extras), row.names(PVH07_Extras), row.names(PVH08_Extras), row.names(PVH09_Extras), row.names(PVH10_Extras), row.names(PVH11_Extras), row.names(PVH12_Extras), row.names(PVH13_Extras), row.names(PVH14_Extras), row.names(PVH15_Extras))| row.names(PVH_UMAP) %in% colnames(PVH16)) set.dim = 20 set.res = 1 set.kparam = c(60) PVH16_Seu = subset(PVH_Seu, cells = row.names(PVH16_Extras)) #ClusterFunc_All_RNA(PVH16_Seu) PVH02_Seu = subset(PVH_Seu, cells = row.names(PVH02_Extras)) #ClusterFunc_All_RNA(PVH02_Seu) DefaultAssay(PVH02_Seu) = "integrated" PVH02_Seu <- FindNeighbors(PVH02_Seu, k.param=60, dims=1:20) PVH02_Seu <- FindClusters(PVH02_Seu, resolution = 1) PVH_17 = subset(PVH02_Seu, idents = c(1)) PVH_17Extra = subset(PVH_UMAP, row.names(PVH_UMAP) %in% colnames(PVH02_Seu) & PVH_UMAP$UMAP_1 > 8 & PVH_UMAP$UMAP_2 < 7.5) PVH_17Final = subset(PVH02_Seu, cells = unique(c(colnames(PVH_17), row.names(PVH_17Extra)))) PVH_02_New = subset(PVH02_Seu, cells = unique(c(colnames(PVH_17), row.names(PVH_17Extra))), invert=T) DefaultAssay(PVH16_Seu) = "integrated" PVH16_Seu <- FindNeighbors(PVH16_Seu, k.param=10, dims=1:20) PVH16_Seu <- FindClusters(PVH16_Seu, resolution = 1) PVH_18 = subset(PVH16_Seu, idents = c(2,4,8)) PVH_16_New = subset(PVH16_Seu, idents = c(2,4,8), invert=T) PVH_Assigns = GenerateMetaData(list("PVH_01" = PVH01_Extras, "PVH_02" = PVH_02_New, "PVH_03" = PVH03_Extras, "PVH_04" = PVH04_Extras, "PVH_05" = PVH05_Extras, "PVH_06" = PVH06Clean, "PVH_07" = PVH07_Extras,"PVH_08" = PVH08_Extras, "PVH_09" = PVH09_Extras, "PVH_10" = PVH10_Extras, "PVH_11" = PVH11_Extras, "PVH_12" = PVH12_Extras,"PVH_13" = PVH13_Extras, "PVH_14" = PVH14_Extras, "PVH_15" = PVH15_Extras, "PVH_16" = PVH_16_New, "PVH_17" = PVH_17Final, "PVH_18" = PVH_18)) #PVH_REST2 = subset(PVH_Seu, cells = PVH_Assigns$Barcodes, invert=T) #CheckInput = PVH_REST #CheckUMAP(PVH_Seu) #load("~/Hypothalamus_Subclustering_2023.RData") save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
TM
GetTMBarcs = subset(MainAssign, MainAssign$Nuclei == "TM") TM_Seu = subset(EdKaZhouHypoNeurons, cells = GetTMBarcs$Barcs) DefaultAssay(TM_Seu) = "integrated" TM_Seu = FindVariableFeatures(TM_Seu) TM_Seu = ScaleData(TM_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(TM_Seu) = "integrated" TM_Seu = RunPCA(TM_Seu, npcs = 30) TM_Seu <- RunHarmony(TM_Seu, group.by.vars = "SampleAdult") TM_Seu <- RunUMAP(TM_Seu, dims = 1:30, spread= 2, reduction = "harmony") DefaultAssay(TM_Seu) = "RNA" set.dim = 30 set.res = 1 set.kparam = c(200) ##ClusterFunc_All_RNA(TM_Seu) DefaultAssay(TM_Seu) = "integrated" TM_Seu <- FindNeighbors(TM_Seu, k.param=100, dims=1:30) TM_Seu <- FindClusters(TM_Seu, resolution = 1) DefaultAssay(TM_Seu) = "RNA" TM_UMAP = as.data.frame(TM_Seu@reductions$umap@cell.embeddings) TM02 = subset(TM_Seu, idents = c(0,4)) TM02Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM02) & TM_UMAP$UMAP_1 < -3 & TM_UMAP$UMAP_2 < 3.5) TM03 = subset(TM_Seu, idents = c(1,6)) TM03Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM03) & TM_UMAP$UMAP_1 > 0 & TM_UMAP$UMAP_1 < 13.5 & TM_UMAP$UMAP_2 > -3 ) TM04 = subset(TM_Seu, idents = c(5)) TM04Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM04) & TM_UMAP$UMAP_1 > -8 & TM_UMAP$UMAP_1 < 3 & TM_UMAP$UMAP_2 > -1) TM05 = subset(TM_Seu, idents = 3) TM05Clean = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM05) & TM_UMAP$UMAP_1 > 13.5 | row.names(TM_UMAP) %in% colnames(TM05) & TM_UMAP$UMAP_1 > 10 & TM_UMAP$UMAP_2 < -2 ) #CheckInput = TM05 #CheckUMAP(TM_Seu) TM_REST = subset(TM_Seu, cells = c(row.names(TM02Clean), row.names(TM03Clean), row.names(TM04Clean), row.names(TM05Clean)), invert=T) #CheckInput = TM03_Extras #CheckUMAP(TM_Seu) TM02_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 < -3 & TM_UMAP$UMAP_2 < 3.5 | row.names(TM_UMAP) %in% row.names(TM02Clean)) TM03_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > 2 & TM_UMAP$UMAP_1 < 13.7 & TM_UMAP$UMAP_2 > -3 | row.names(TM_UMAP) %in% row.names(TM03Clean)) TM04_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > -10 & TM_UMAP$UMAP_1 < 5 & ! row.names(TM_UMAP) %in% c(row.names(TM02_Extras), row.names(TM03_Extras)) | row.names(TM_UMAP) %in% row.names(TM04Clean)) TM05_Extras = subset(TM_UMAP, row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > 2 & TM_UMAP$UMAP_1 > 13.7 | row.names(TM_UMAP) %in% colnames(TM_REST) & TM_UMAP$UMAP_1 > 5 & TM_UMAP$UMAP_2 < -2 | row.names(TM_UMAP) %in% row.names(TM05Clean)) TM_Assigns = GenerateMetaData_Barcodes(list( "TM_02" = TM02_Extras, "TM_03" = TM03_Extras, "TM_04" = TM04_Extras,"TM_05" = TM05_Extras)) #TM_01_Rest = subset(TM_Seu, cells = TM_Assigns$Barcodes, invert=T) #CheckInput = TM_01_Rest #CheckUMAP(TM_Seu) TM_Assigns$Dups = duplicated(TM_Assigns$Barcodes) | duplicated(TM_Assigns$Barcodes, fromLast=T) TM_Assigns_T = subset(TM_Assigns, TM_Assigns$Dups == T) unique(TM_Assigns_T$Pop) TM_Assigns = GenerateMetaData(list("TM_02" = TM02_Extras, "TM_03" = TM03_Extras, "TM_04" = TM04_Extras,"TM_05" = TM05_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
VMH
GetVMHBarcs = subset(MainAssign, MainAssign$Nuclei == "VMH") VMH_Seu = subset(EdKaZhouHypoNeurons, cells = GetVMHBarcs$Barcs) DefaultAssay(VMH_Seu) = "integrated" VMH_Seu = FindVariableFeatures(VMH_Seu) VMH_Seu = ScaleData(VMH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(VMH_Seu) = "integrated" VMH_Seu = RunPCA(VMH_Seu, npcs = 20) VMH_Seu <- RunHarmony(VMH_Seu, group.by.vars = "SampleAdult") VMH_Seu <- RunUMAP(VMH_Seu, dims = 1:20, spread= 5, reduction = "harmony") DefaultAssay(VMH_Seu) = "RNA" set.dim = c(20) set.res = 1 set.kparam = c(50) #ClusterFunc_All_RNA(VMH_Seu) DefaultAssay(VMH_Seu) = "integrated" VMH_Seu <- FindNeighbors(VMH_Seu, k.param=50, dims=1:20) VMH_Seu <- FindClusters(VMH_Seu, resolution = 1) DefaultAssay(VMH_Seu) = "RNA" VMH_UMAP = as.data.frame(VMH_Seu@reductions$umap@cell.embeddings) VMH01 = subset(VMH_Seu, idents = c(0)) VMH01Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH01) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > -8 & VMH_UMAP$UMAP_2 < 16) VMH02 = subset(VMH_Seu, idents = 2) VMH02Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH02) & VMH_UMAP$UMAP_1 > 2 & VMH_UMAP$UMAP_2 > 17 | row.names(VMH_UMAP) %in% colnames(VMH02) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > 15) VMH03 = subset(VMH_Seu, idents = 3) VMH03Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH03) & VMH_UMAP$UMAP_2 < -25) VMH04 = subset(VMH_Seu, idents = 4) VMH04Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH04)& VMH_UMAP$UMAP_1 > -15 & VMH_UMAP$UMAP_1 < -1 & VMH_UMAP$UMAP_2 > -11) VMH05 = subset(VMH_Seu, idents = c(1,5)) VMH05Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH05) & VMH_UMAP$UMAP_1 > -7 & VMH_UMAP$UMAP_1 < 4 & VMH_UMAP$UMAP_2 > -1 & VMH_UMAP$UMAP_2 < 11 | row.names(VMH_UMAP) %in% colnames(VMH05) & VMH_UMAP$UMAP_1 > -2 & VMH_UMAP$UMAP_1 < 7 & VMH_UMAP$UMAP_2 > -5 & VMH_UMAP$UMAP_2 < 0) VMH06Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH05)& VMH_UMAP$UMAP_1 > 4 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > -2 & VMH_UMAP$UMAP_2 < 20 | row.names(VMH_UMAP) %in% colnames(VMH05)& VMH_UMAP$UMAP_1 > 0 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > 11 & VMH_UMAP$UMAP_2 < 20 ) VMH07 = subset(VMH_Seu, idents = 6) VMH07Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH07)& VMH_UMAP$UMAP_1 < -7) VMH08 = subset(VMH_Seu, idents = c(7)) VMH08Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH08) & VMH_UMAP$UMAP_1 > -23 & VMH_UMAP$UMAP_1 < -10 & VMH_UMAP$UMAP_2 > 10) VMH09 = subset(VMH_Seu, idents = 8) VMH09Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH09) & VMH_UMAP$UMAP_1 < -19 & VMH_UMAP$UMAP_2 < 18) VMH10 = subset(VMH_Seu, idents = 9) VMH10Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH10) & VMH_UMAP$UMAP_1 > -2 & VMH_UMAP$UMAP_1 < 15 & VMH_UMAP$UMAP_2 > -25) VMH11 = subset(VMH_Seu, idents = 10) VMH11Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH11) & VMH_UMAP$UMAP_1 > -21 & VMH_UMAP$UMAP_1 < -5 & VMH_UMAP$UMAP_2 > -6 & VMH_UMAP$UMAP_2 < 8) VMH12 = subset(VMH_Seu, idents = 11) VMH12Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH12) & VMH_UMAP$UMAP_1 < -18) VMH13 = subset(VMH_Seu, idents = 12) VMH13Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH13) & VMH_UMAP$UMAP_2 < -5) VMH14 = subset(VMH_Seu, idents = 13) VMH14Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH14) & VMH_UMAP$UMAP_2 < -5) VMH15 = subset(VMH_Seu, idents = 14) VMH15Clean = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH15) & VMH_UMAP$UMAP_1 > -10& VMH_UMAP$UMAP_1 < 0) #CheckInput = VMH04Clean #CheckUMAP(VMH_Seu) VMH_REST = subset(VMH_Seu, cells = c(row.names(VMH01Clean), row.names(VMH02Clean), row.names(VMH03Clean), row.names(VMH04Clean), row.names(VMH05Clean),row.names(VMH06Clean),row.names(VMH07Clean), row.names(VMH08Clean), row.names(VMH09Clean), row.names(VMH10Clean), row.names(VMH11Clean), row.names(VMH12Clean), row.names(VMH13Clean), row.names(VMH14Clean),row.names(VMH15Clean)), invert=T) #CheckInput = VMH06_Extras #CheckUMAP(VMH_Seu) ## VMH01_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > -8 & VMH_UMAP$UMAP_2 < 14 & VMH_UMAP$UMAP_2 > -7 | row.names(VMH_UMAP) %in% row.names(VMH01Clean)) VMH03_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -1 & VMH_UMAP$UMAP_2 < -25 | row.names(VMH_UMAP) %in% row.names(VMH03Clean)) VMH05_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -2.5 & VMH_UMAP$UMAP_1 < 5 & VMH_UMAP$UMAP_2 > -10 & VMH_UMAP$UMAP_2 < 0 | row.names(VMH_UMAP) %in% row.names(VMH05Clean)) VMH06_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 4 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > -2 & VMH_UMAP$UMAP_2 < 23 & ! row.names(VMH_UMAP) %in% row.names(VMH05_Extras) | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -1 & VMH_UMAP$UMAP_1 < 10 & VMH_UMAP$UMAP_2 > 11 & VMH_UMAP$UMAP_2 < 23 | row.names(VMH_UMAP) %in% row.names(VMH06Clean)) VMH10_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -2 & VMH_UMAP$UMAP_1 < 16 & VMH_UMAP$UMAP_2 > -26 & VMH_UMAP$UMAP_2 < -10 | row.names(VMH_UMAP) %in% row.names(VMH10Clean)) VMH08_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -20 & VMH_UMAP$UMAP_1 < -8 & VMH_UMAP$UMAP_2 > 15 & VMH_UMAP$UMAP_2 < 21 | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -18 & VMH_UMAP$UMAP_1 < -8 & VMH_UMAP$UMAP_2 > 8 | row.names(VMH_UMAP) %in% row.names(VMH08Clean)) VMH13_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 30 & VMH_UMAP$UMAP_2 < -10 | row.names(VMH_UMAP) %in% row.names(VMH13Clean)) VMH14_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 16& VMH_UMAP$UMAP_1 < 30 & VMH_UMAP$UMAP_2 < -5 | row.names(VMH_UMAP) %in% row.names(VMH14Clean)) VMH15_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -8 & VMH_UMAP$UMAP_1 < -1 & VMH_UMAP$UMAP_2 < -25 | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -5 & VMH_UMAP$UMAP_1 < -2 & VMH_UMAP$UMAP_2 < -21 | row.names(VMH_UMAP) %in% row.names(VMH15Clean)) VMH16_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -7 & VMH_UMAP$UMAP_1 < -2 & VMH_UMAP$UMAP_2 > -22 & VMH_UMAP$UMAP_2 < -11 | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -9 & VMH_UMAP$UMAP_1 < -7 & VMH_UMAP$UMAP_2 > -15 & VMH_UMAP$UMAP_2 < -11) VMH02_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 2 & VMH_UMAP$UMAP_2 > 17 & ! row.names(VMH_UMAP) %in% row.names(VMH06_Extras) | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > 10 & VMH_UMAP$UMAP_2 > 14 | row.names(VMH_UMAP) %in% row.names(VMH02Clean)) VMH07_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -8 & VMH_UMAP$UMAP_2 > -27 & VMH_UMAP$UMAP_2 < -10 & ! row.names(VMH_UMAP) %in% row.names(VMH16_Extras) | row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -7 & VMH_UMAP$UMAP_2 > -25 & VMH_UMAP$UMAP_2 < -21 & ! row.names(VMH_UMAP) %in% row.names(VMH16_Extras) | row.names(VMH_UMAP) %in% row.names(VMH07Clean)) VMH09_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -18 & VMH_UMAP$UMAP_2 > 3 & VMH_UMAP$UMAP_2 < 17 & ! row.names(VMH_UMAP) %in% row.names(VMH08_Extras) | row.names(VMH_UMAP) %in% row.names(VMH09Clean)) VMH11_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 > -20 & VMH_UMAP$UMAP_1 < -10.7 & VMH_UMAP$UMAP_2 > -3.5 & VMH_UMAP$UMAP_2 < 7 & ! row.names(VMH_UMAP) %in% row.names(VMH09_Extras) | row.names(VMH_UMAP) %in% row.names(VMH11Clean)) VMH04_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -1 & VMH_UMAP$UMAP_2 > -12 & VMH_UMAP$UMAP_2 < 0 & ! row.names(VMH_UMAP) %in% c(row.names(VMH07_Extras), row.names(VMH16_Extras), row.names(VMH11_Extras), row.names(VMH05_Extras)) | row.names(VMH_UMAP) %in% row.names(VMH04Clean)) VMH12_Extras = subset(VMH_UMAP, row.names(VMH_UMAP) %in% colnames(VMH_REST) & VMH_UMAP$UMAP_1 < -18 & VMH_UMAP$UMAP_2 > -10 & ! row.names(VMH_UMAP) %in% c(row.names(VMH09_Extras), row.names(VMH08_Extras), row.names(VMH10_Extras)) | row.names(VMH_UMAP) %in% row.names(VMH12Clean)) CheckInput = VMH_Rest2 CheckUMAP(VMH_Seu) VMH_Assigns_V1 = GenerateMetaData_Barcodes(list("VMH_01" = VMH01_Extras, "VMH_02" = VMH02_Extras,"VMH_03" = VMH03_Extras, "VMH_04" = VMH04_Extras, "VMH_05" = VMH05_Extras, "VMH_06" = VMH06_Extras, "VMH_07" = VMH07_Extras, "VMH_08" = VMH08_Extras, "VMH_09" = VMH09_Extras, "VMH_10" = VMH10_Extras, "VMH_11" = VMH11_Extras, "VMH_12" = VMH12_Extras,"VMH_13" = VMH13_Extras, "VMH_14" = VMH14_Extras, "VMH_15" = VMH15_Extras, "VMH_16" = VMH16_Extras)) VMH_Rest2 = subset(VMH_Seu, cells = VMH_Assigns_V1$Barcodes, invert=T) VMH_Assigns_V1$Dups = duplicated(VMH_Assigns_V1$Barcodes) | duplicated(VMH_Assigns_V1$Barcodes, fromLast=T) VMH_Assigns_T = subset(VMH_Assigns_V1, VMH_Assigns_V1$Dups == T) unique(VMH_Assigns_T$Pop) VMH_Assigns = GenerateMetaData(list("VMH_01" = VMH01_Extras, "VMH_02" = VMH02_Extras,"VMH_03" = VMH03_Extras, "VMH_04" = VMH04_Extras, "VMH_05" = VMH05_Extras, "VMH_06" = VMH06_Extras, "VMH_07" = VMH07_Extras, "VMH_08" = VMH08_Extras, "VMH_09" = VMH09_Extras, "VMH_10" = VMH10_Extras, "VMH_11" = VMH11_Extras, "VMH_12" = VMH12_Extras,"VMH_13" = VMH13_Extras, "VMH_14" = VMH14_Extras, "VMH_15" = VMH15_Extras, "VMH_16" = VMH16_Extras, "VMH_05" = VMH_Rest2)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
SMN
GetSMNBarcs = subset(MainAssign, MainAssign$Nuclei == "SMN") SMN_Seu = subset(EdKaZhouHypoNeurons, cells = GetSMNBarcs$Barcs) DefaultAssay(SMN_Seu) = "integrated" SMN_Seu = FindVariableFeatures(SMN_Seu) SMN_Seu = ScaleData(SMN_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(SMN_Seu) = "integrated" SMN_Seu = RunPCA(SMN_Seu, npcs = 30) SMN_Seu <- RunUMAP(SMN_Seu, dims = 1:30, spread= 2) DefaultAssay(SMN_Seu) = "RNA" set.dim = 30 set.res = 1 set.kparam = c(50) ##ClusterFunc_All_RNA(SMN_Seu) DefaultAssay(SMN_Seu) = "integrated" SMN_Seu <- FindNeighbors(SMN_Seu, k.param=50, dims=1:30) SMN_Seu <- FindClusters(SMN_Seu, resolution = 1) DefaultAssay(SMN_Seu) = "RNA" SMN_UMAP = as.data.frame(SMN_Seu@reductions$umap@cell.embeddings) SMN01 = subset(SMN_Seu, idents = c(0, 1, 11, 16)) SMN01Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN01) & SMN_UMAP$UMAP_1 > -9 & SMN_UMAP$UMAP_1 < 1.5 & SMN_UMAP$UMAP_2 > -9 & SMN_UMAP$UMAP_2 < 1 | row.names(SMN_UMAP) %in% colnames(SMN01) & SMN_UMAP$UMAP_1 > -10 & SMN_UMAP$UMAP_1 < -5 & SMN_UMAP$UMAP_2 < -5) SMN02 = subset(SMN_Seu, idents = c(2, 9)) SMN03 = subset(SMN_Seu, idents = 3) SMN03Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN03) & SMN_UMAP$UMAP_2 > -8) SMN04 = subset(SMN_Seu, idents = 4) SMN04Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN04) & SMN_UMAP$UMAP_1 > -2 & SMN_UMAP$UMAP_1 < 5) SMN05 = subset(SMN_Seu, idents = c(5, 8, 13, 15,18)) SMN05Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN05) & SMN_UMAP$UMAP_2 < 10) SMN06 = subset(SMN_Seu, idents = 6) SMN07 = subset(SMN_Seu, idents = 7) SMN07Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN07) & SMN_UMAP$UMAP_1 > 5) SMN08 = subset(SMN_Seu, idents = 10) SMN08Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN08) & SMN_UMAP$UMAP_1 > 5 & SMN_UMAP$UMAP_2 < -5) SMN09 = subset(SMN_Seu, idents = c(12,14)) SMN09Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN09) & SMN_UMAP$UMAP_1 > -4) SMN10 = subset(SMN_Seu, idents = 17) SMN10Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN10) & SMN_UMAP$UMAP_2 > 6 & SMN_UMAP$UMAP_2 < 11 & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 10) #CheckInput = SMN_REST #CheckUMAP(SMN_Seu) SMN_REST = subset(SMN_Seu, cells = c(colnames(SMN02), colnames(SMN06), row.names(SMN01Clean), row.names(SMN03Clean), row.names(SMN04Clean), row.names(SMN05Clean),row.names(SMN07Clean), row.names(SMN08Clean), row.names(SMN09Clean),row.names(SMN10Clean)), invert=T) #CheckInput = SMN10_Extras #CheckUMAP(SMN_Seu) SMN01_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -10 & SMN_UMAP$UMAP_1 < 1.5 & SMN_UMAP$UMAP_2 > -9 & SMN_UMAP$UMAP_2 < 1 | row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -10 & SMN_UMAP$UMAP_1 < -5 & SMN_UMAP$UMAP_2 < -5 | row.names(SMN_UMAP) %in% row.names(SMN01Clean)) SMN02_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 0 & SMN_UMAP$UMAP_1 < 10 & SMN_UMAP$UMAP_2 > 11 | row.names(SMN_UMAP) %in% colnames(SMN02)) SMN03_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 < -10 & SMN_UMAP$UMAP_2 < 0 & ! row.names(SMN_UMAP) %in% row.names(SMN01_Extras)| row.names(SMN_UMAP) %in% row.names(SMN03Clean)) SMN04_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 0 & SMN_UMAP$UMAP_1 < 5 & SMN_UMAP$UMAP_2 < -5| row.names(SMN_UMAP) %in% row.names(SMN04Clean)) SMN05_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -2 & SMN_UMAP$UMAP_2 > -5 & SMN_UMAP$UMAP_2 < 7 & ! row.names(SMN_UMAP) %in% row.names(SMN01_Extras) | row.names(SMN_UMAP) %in% row.names(SMN05Clean)) SMN06_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 < -10 & SMN_UMAP$UMAP_2 > 10 | row.names(SMN_UMAP) %in% colnames(SMN06)) SMN07_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 10 & SMN_UMAP$UMAP_2 > 5 & ! row.names(SMN_UMAP) %in% row.names(SMN02_Extras) | row.names(SMN_UMAP) %in% row.names(SMN07Clean)) SMN08_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > 5 & SMN_UMAP$UMAP_2 < -5 | row.names(SMN_UMAP) %in% row.names(SMN08Clean)) SMN09_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_1 > -6 & SMN_UMAP$UMAP_1 < 4 & SMN_UMAP$UMAP_2 < -4 & ! row.names(SMN_UMAP) %in% c(row.names(SMN01_Extras), row.names(SMN04_Extras)) | row.names(SMN_UMAP) %in% row.names(SMN09Clean)) SMN10_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST) & SMN_UMAP$UMAP_2 > 6 & SMN_UMAP$UMAP_2 < 11 & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 10 | row.names(SMN_UMAP) %in% row.names(SMN10Clean)) SMN_Assigns = GenerateMetaData_Barcodes(list("SMN_01" = SMN01_Extras, "SMN_02" = SMN02_Extras, "SMN_03" = SMN03_Extras, "SMN_04" = SMN04_Extras, "SMN_05" = SMN05_Extras, "SMN_06" = SMN06_Extras, "SMN_07" = SMN07_Extras, "SMN_08" = SMN08_Extras, "SMN_09" = SMN09_Extras, "SMN_10" = SMN10_Extras)) #SMN_REST2 = subset(SMN_Seu, cells = SMN_Assigns$Barcodes, invert=T) #CheckInput = SMN_REST2 #CheckUMAP(SMN_Seu) SMN01_Recluster = subset(SMN_Seu, cells = row.names(SMN01_Extras)) set.dim = 30 set.res = 1 set.kparam = c(50, 80) ##ClusterFunc_All_RNA(SMN01_Recluster) DefaultAssay(SMN01_Recluster) = "integrated" SMN01_Recluster <- FindNeighbors(SMN01_Recluster, k.param=40, dims=1:30) SMN01_Recluster <- FindClusters(SMN01_Recluster, resolution = 1) DefaultAssay(SMN01_Recluster) = "RNA" SMN11 = subset(SMN01_Recluster, idents = c(1,9, 5)) SMN12 = subset(SMN01_Recluster, idents = c(4)) SMN13 = subset(SMN01_Recluster, idents = c(2)) SMN11Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN11) & SMN_UMAP$UMAP_2 > -10 & SMN_UMAP$UMAP_2 < -5 & SMN_UMAP$UMAP_1 < -1) SMN12Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN12) & SMN_UMAP$UMAP_2 < -10) SMN13Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN13) & SMN_UMAP$UMAP_1 < -7 & SMN_UMAP$UMAP_2 > -11) SMN_REST3 = subset(SMN01_Recluster, cells = c(row.names(SMN11Clean), row.names(SMN12Clean), row.names(SMN13Clean)), invert=T) SMN11_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_2 > -10 & SMN_UMAP$UMAP_2 < -6 & SMN_UMAP$UMAP_1 < -1 & SMN_UMAP$UMAP_1 > -7.5 | row.names(SMN_UMAP) %in% row.names(SMN11Clean)) SMN12_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_2 < -10 | row.names(SMN_UMAP) %in% row.names(SMN12Clean)) SMN13_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_1 < -8 & ! row.names(SMN_UMAP) %in% c(row.names(SMN11_Extras), row.names(SMN12_Extras)) | row.names(SMN_UMAP) %in% colnames(SMN_REST3) & SMN_UMAP$UMAP_1 < -6 & SMN_UMAP$UMAP_2 < -6 & ! row.names(SMN_UMAP) %in% c(row.names(SMN11_Extras), row.names(SMN12_Extras)) | row.names(SMN_UMAP) %in% row.names(SMN13Clean)) SMN01_New = subset(SMN01_Recluster, cells = c(row.names(SMN11_Extras), row.names(SMN12_Extras), row.names(SMN13_Extras)), invert=T) SMN05_Seu = subset(SMN_Seu, cells = row.names(SMN05_Extras)) set.dim = 30 set.res = 1 set.kparam = c(40) #ClusterFunc_All_RNA(SMN05_Seu) DefaultAssay(SMN05_Seu) = "integrated" SMN05_Seu <- FindNeighbors(SMN05_Seu, k.param=40, dims=1:30) SMN05_Seu <- FindClusters(SMN05_Seu, resolution = 1) SMN_14 = subset(SMN05_Seu, idents = c(0,1)) SMN_15 = subset(SMN05_Seu, idents = c(2,9)) SMN_16 = subset(SMN05_Seu, idents = c(3)) SMN_17 = subset(SMN05_Seu, idents = c(4)) SMN_18 = subset(SMN05_Seu, idents = c(5)) SMN_19 = subset(SMN05_Seu, idents = c(6)) SMN_20 = subset(SMN05_Seu, idents = c(7)) SMN_05_New = subset(SMN05_Seu, idents = c(8)) SMN14Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_14) & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 7 & SMN_UMAP$UMAP_2 > 0) SMN_15 SMN16Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_16) & SMN_UMAP$UMAP_1 > 6 & SMN_UMAP$UMAP_2 > -2 & SMN_UMAP$UMAP_2 < 2) SMN17Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_17) & SMN_UMAP$UMAP_1 > 6.5 & SMN_UMAP$UMAP_1 < 11 & SMN_UMAP$UMAP_2 > 2) SMN18Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_18) & SMN_UMAP$UMAP_1 < 4.5 & SMN_UMAP$UMAP_2 < 4.5) SMN_19 SMN20Clean = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_20) & SMN_UMAP$UMAP_1 > 8) SMN_05_New SMN_REST4 = subset(SMN05_Seu, cells = c(row.names(SMN14Clean), row.names(SMN16Clean), row.names(SMN17Clean), row.names(SMN18Clean), row.names(SMN20Clean), colnames(SMN_15), colnames(SMN_19), colnames(SMN_05_New)), invert=T) SMN14Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 3 & SMN_UMAP$UMAP_1 < 6.5 & SMN_UMAP$UMAP_2 > 1.6 | row.names(SMN_UMAP) %in% row.names(SMN14Clean) ) SMN15Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 8 & SMN_UMAP$UMAP_2 < 0 & SMN_UMAP$UMAP_2 > -2 | row.names(SMN_UMAP) %in% colnames(SMN_15) ) SMN16Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 6.5 & SMN_UMAP$UMAP_2 < 2.3 & SMN_UMAP$UMAP_2 > -1 & ! row.names(SMN_UMAP) %in% row.names(SMN15Extras) | row.names(SMN_UMAP) %in% row.names(SMN16Clean) ) SMN17Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 6.5 & SMN_UMAP$UMAP_1 < 11 & SMN_UMAP$UMAP_2 > 2 | row.names(SMN_UMAP) %in% row.names(SMN17Clean) ) SMN18Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 3.7 & SMN_UMAP$UMAP_2 < 3 & SMN_UMAP$UMAP_2 > 0 & ! row.names(SMN_UMAP) %in% row.names(SMN14Extras) | row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 5 & SMN_UMAP$UMAP_2 < 2 & SMN_UMAP$UMAP_2 > 0 & ! row.names(SMN_UMAP) %in% row.names(SMN14Extras) | row.names(SMN_UMAP) %in% row.names(SMN18Clean) ) SMN19Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 4 & ! row.names(SMN_UMAP) %in% c(row.names(SMN14Extras), row.names(SMN18Extras)) | row.names(SMN_UMAP) %in% colnames(SMN_19) ) SMN20Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 > 8 & SMN_UMAP$UMAP_2 > 2.5 & ! row.names(SMN_UMAP) %in% row.names(SMN17Extras) | row.names(SMN_UMAP) %in% row.names(SMN20Clean) ) SMN05_New_Extras = subset(SMN_UMAP, row.names(SMN_UMAP) %in% colnames(SMN_REST4) & SMN_UMAP$UMAP_1 < 10 & SMN_UMAP$UMAP_2 < 1 & SMN_UMAP$UMAP_1 > 4 & ! row.names(SMN_UMAP) %in% c(row.names(SMN15Extras), row.names(SMN16Extras)) | row.names(SMN_UMAP) %in% colnames(SMN_05_New) ) SMN_Assigns = GenerateMetaData_Barcodes(list("SMN_01" = SMN01_New, "SMN_02" = SMN02_Extras, "SMN_03" = SMN03_Extras, "SMN_04" = SMN04_Extras, "SMN_05" = SMN05_New_Extras, "SMN_06" = SMN06_Extras, "SMN_07" = SMN07_Extras, "SMN_08" = SMN08_Extras, "SMN_09" = SMN09_Extras, "SMN_10" = SMN10_Extras, "SMN_11" = SMN11_Extras, "SMN_12" = SMN12_Extras, "SMN_13" = SMN13_Extras, "SMN_14" = SMN14Extras, "SMN_15" = SMN15Extras, "SMN_16" = SMN16Extras, "SMN_17" = SMN17Extras, "SMN_18" = SMN18Extras, "SMN_19" = SMN19Extras, "SMN_20" = SMN20Extras)) #CheckInput = SMN05_New_Extras #CheckUMAP(SMN05_Seu) SMN_Assigns$Dups = duplicated(SMN_Assigns$Barcodes) | duplicated(SMN_Assigns$Barcodes, fromLast=T) SMN_Assigns_T = subset(SMN_Assigns, SMN_Assigns$Dups == T) unique(SMN_Assigns_T$Pop) SMN_Assigns = GenerateMetaData(list("SMN_01" = SMN01_New, "SMN_02" = SMN02_Extras, "SMN_03" = SMN03_Extras, "SMN_04" = SMN04_Extras, "SMN_05" = SMN05_New_Extras, "SMN_06" = SMN06_Extras, "SMN_07" = SMN07_Extras, "SMN_08" = SMN08_Extras, "SMN_09" = SMN09_Extras, "SMN_10" = SMN10_Extras, "SMN_11" = SMN11_Extras, "SMN_12" = SMN12_Extras, "SMN_13" = SMN13_Extras, "SMN_14" = SMN14Extras, "SMN_15" = SMN15Extras, "SMN_16" = SMN16Extras, "SMN_17" = SMN17Extras, "SMN_18" = SMN18Extras, "SMN_19" = SMN19Extras, "SMN_20" = SMN20Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
LH
GetLHBarcs = subset(MainAssign, MainAssign$Nuclei == "LH") LH_Seu = subset(EdKaZhouHypoNeurons, cells = GetLHBarcs$Barcs) DefaultAssay(LH_Seu) = "integrated" LH_Seu = FindVariableFeatures(LH_Seu) LH_Seu = ScaleData(LH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(LH_Seu) = "integrated" LH_Seu = RunPCA(LH_Seu, npcs = 20) LH_Seu <- RunHarmony(LH_Seu, group.by.vars = "SampleAdult") LH_Seu <- RunUMAP(LH_Seu, dims = 1:20, spread= 5, reduction = "harmony") DefaultAssay(LH_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(5) #ClusterFunc_All_RNA(LH_Seu) DefaultAssay(LH_Seu) = "integrated" LH_Seu <- FindNeighbors(LH_Seu, k.param=5, dims=1:20) LH_Seu <- FindClusters(LH_Seu, resolution = 1) DefaultAssay(LH_Seu) = "RNA" #CheckInput = LH08 #CheckUMAP(LH_Seu) LH_UMAP = as.data.frame(LH_Seu@reductions$umap@cell.embeddings) LH01 = subset(LH_Seu, idents = c(0,3,4)) LH01Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH01) & LH_UMAP$UMAP_1 < -3 & LH_UMAP$UMAP_2 > -9.5 & LH_UMAP$UMAP_2 < 10) LH02 = subset(LH_Seu, idents = 1) LH02Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH02) & LH_UMAP$UMAP_1 < -3 & LH_UMAP$UMAP_2 > 10 & LH_UMAP$UMAP_2 < 20) LH03 = subset(LH_Seu, idents = c(2)) LH03Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH03) & LH_UMAP$UMAP_1 > -3 & LH_UMAP$UMAP_1 < 8 & LH_UMAP$UMAP_2 > -8 & LH_UMAP$UMAP_2 < 5) LH04 = subset(LH_Seu, idents = 5) LH04Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH04) & LH_UMAP$UMAP_1 > -5 & LH_UMAP$UMAP_1 < 10) LH05 = subset(LH_Seu, idents = c(6,8)) LH05Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH05) & LH_UMAP$UMAP_1 > 8 & LH_UMAP$UMAP_1 < 21 & LH_UMAP$UMAP_2 > -20 & LH_UMAP$UMAP_2 < -7) LH06 = subset(LH_Seu, idents = 7) LH06Clean = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH06) & LH_UMAP$UMAP_1 > 10 & LH_UMAP$UMAP_2 > 0) LH07 = subset(LH_Seu, idents = 10) LH08 = subset(LH_Seu, idents = 11) #CheckInput = LH10Clean #CheckUMAP(LH_Seu) LH_REST = subset(LH_Seu, cells = c(row.names(LH01Clean), row.names(LH02Clean), row.names(LH03Clean), row.names(LH04Clean), row.names(LH05Clean),row.names(LH06Clean), colnames(LH07), colnames(LH08)), invert=T) #CheckInput = LH08_Extras #CheckUMAP(LH_Seu) LH01_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 < -4 & LH_UMAP$UMAP_2 < 9 & LH_UMAP$UMAP_2 > -7 | row.names(LH_UMAP) %in% row.names(LH01Clean)) LH02_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 < -4 & LH_UMAP$UMAP_2 > 9 | row.names(LH_UMAP) %in% row.names(LH02Clean)) LH03_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > -1 & LH_UMAP$UMAP_1 < 9 & LH_UMAP$UMAP_2 < 3 & LH_UMAP$UMAP_2 > -9 | row.names(LH_UMAP) %in% row.names(LH03Clean)) LH04_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > -6 & LH_UMAP$UMAP_1 < 8 & LH_UMAP$UMAP_2 > 15 | row.names(LH_UMAP) %in% row.names(LH04Clean)) LH05_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 8 & LH_UMAP$UMAP_1 < 20 & LH_UMAP$UMAP_2 < -9 & LH_UMAP$UMAP_2 > -16 | row.names(LH_UMAP) %in% row.names(LH05Clean)) LH06_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 20 & LH_UMAP$UMAP_2 > 0 | row.names(LH_UMAP) %in% row.names(LH06Clean)) LH08_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 8 & LH_UMAP$UMAP_1 < 13 & LH_UMAP$UMAP_2 > -8 & LH_UMAP$UMAP_2 < -2 | row.names(LH_UMAP) %in% colnames(LH08)) LH09_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 > 10 & LH_UMAP$UMAP_1 < 20 & LH_UMAP$UMAP_2 > 0 & LH_UMAP$UMAP_2 < 10) LH10_Extras = subset(LH_UMAP, row.names(LH_UMAP) %in% colnames(LH_REST) & LH_UMAP$UMAP_1 < -7 & LH_UMAP$UMAP_2 < -10) LH_Assigns = GenerateMetaData_Barcodes(list( "LH_01" = LH01_Extras, "LH_02" = LH02_Extras, "LH_03" = LH03_Extras, "LH_04" = LH04_Extras, "LH_05" = LH05_Extras, "LH_06" = LH06_Extras, "LH_07" = LH07, "LH_08" = LH08_Extras, "LH_09" = LH09_Extras, "LH_10" = LH10_Extras)) LH_REST2 = subset(LH_Seu, cells = LH_Assigns$Barcodes, invert=T) #CheckInput = LH10_Extras #CheckUMAP(LH_Seu) LH_Assigns$Dups = duplicated(LH_Assigns$Barcodes) | duplicated(LH_Assigns$Barcodes, fromLast=T) LH_Assigns_T = subset(LH_Assigns, LH_Assigns$Dups == T) unique(LH_Assigns_T$Pop) LH_Assigns = GenerateMetaData(list( "LH_01" = LH01_Extras, "LH_02" = LH02_Extras, "LH_03" = LH03_Extras, "LH_04" = LH04_Extras, "LH_05" = LH05_Extras, "LH_06" = LH06_Extras, "LH_07" = LH07, "LH_08" = LH08_Extras, "LH_09" = LH09_Extras, "LH_10" = LH10_Extras, "LH_11" = LH_REST2)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
SCN
GetSCNBarcs = subset(MainAssign, MainAssign$Nuclei == "SCN") SCN_Seu = subset(EdKaZhouHypoNeurons, cells = GetSCNBarcs$Barcs) DefaultAssay(SCN_Seu) = "integrated" SCN_Seu = FindVariableFeatures(SCN_Seu) SCN_Seu = ScaleData(SCN_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(SCN_Seu) = "integrated" SCN_Seu = RunPCA(SCN_Seu, npcs = 20) SCN_Seu <- RunUMAP(SCN_Seu, dims = 1:20, spread= 2) DefaultAssay(SCN_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(50) ##ClusterFunc_All_RNA(SCN_Seu) DefaultAssay(SCN_Seu) = "integrated" SCN_Seu <- FindNeighbors(SCN_Seu, k.param=50, dims=1:20) SCN_Seu <- FindClusters(SCN_Seu, resolution = 1) DefaultAssay(SCN_Seu) = "RNA" #CheckInput = SCN08Clean #CheckUMAP(SCN_Seu) SCN_UMAP = as.data.frame(SCN_Seu@reductions$umap@cell.embeddings) SCN01 = subset(SCN_Seu, idents = c(0)) SCN01Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN01) & SCN_UMAP$UMAP_1 > -3 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 > -8 & SCN_UMAP$UMAP_2 < 1) SCN02 = subset(SCN_Seu, idents = c(1,6,13,22)) SCN02Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN02) & SCN_UMAP$UMAP_1 < -6 & SCN_UMAP$UMAP_2 < 7) SCN03 = subset(SCN_Seu, idents = c(2,20)) SCN03Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN03) & SCN_UMAP$UMAP_1 > 4 & SCN_UMAP$UMAP_1 < 12 & SCN_UMAP$UMAP_2 > 5) SCN04 = subset(SCN_Seu, idents = c(3,18)) SCN04Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN04) & SCN_UMAP$UMAP_1 < -3 & SCN_UMAP$UMAP_1 > -11 & SCN_UMAP$UMAP_2 > 0) SCN05 = subset(SCN_Seu, idents = 4) SCN05Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN05) & SCN_UMAP$UMAP_1 > 6 & SCN_UMAP$UMAP_2 < 0) SCN06 = subset(SCN_Seu, idents = 5) SCN06Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN06) & SCN_UMAP$UMAP_1 < 3 & SCN_UMAP$UMAP_2 > -14 & SCN_UMAP$UMAP_2 < -8.5) SCN07 = subset(SCN_Seu, idents = c(7,15)) SCN07Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN07) & SCN_UMAP$UMAP_1 > -4 & SCN_UMAP$UMAP_1 < 7 & SCN_UMAP$UMAP_2 > -3 & SCN_UMAP$UMAP_2 < 5) SCN08 = subset(SCN_Seu, idents = c(8)) SCN08Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN08) & SCN_UMAP$UMAP_1 > 5 & SCN_UMAP$UMAP_1 < 11 & SCN_UMAP$UMAP_2 > -2 & SCN_UMAP$UMAP_2 < 8) SCN09 = subset(SCN_Seu, idents = c(9)) SCN09Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN09) & SCN_UMAP$UMAP_1 > 0 & SCN_UMAP$UMAP_2 < -5 & SCN_UMAP$UMAP_2 > -10) SCN10 = subset(SCN_Seu, idents = c(10)) SCN10Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN10) & SCN_UMAP$UMAP_1 > -5 & SCN_UMAP$UMAP_1 < 0) SCN11 = subset(SCN_Seu, idents = c(11)) SCN11Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN11) & SCN_UMAP$UMAP_1 > 1 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 > -7 & SCN_UMAP$UMAP_2 < 0) SCN12 = subset(SCN_Seu, idents = c(12)) SCN12Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN12) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_2 < 4) SCN13 = subset(SCN_Seu, idents = c(14)) SCN13Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN13) & SCN_UMAP$UMAP_1 < 4 ) SCN14 = subset(SCN_Seu, idents = c(16)) SCN14Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN14) & SCN_UMAP$UMAP_2 > 4.5 ) SCN15 = subset(SCN_Seu, idents = c(17)) SCN15Clean = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN15) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_2 > 0) SCN16 = subset(SCN_Seu, idents = c(19)) SCN17 = subset(SCN_Seu, idents = c(21)) #CheckInput = SCN14Clean #CheckUMAP(SCN_Seu) SCN_REST = subset(SCN_Seu, cells = c(row.names(SCN01Clean), row.names(SCN02Clean), row.names(SCN03Clean), row.names(SCN04Clean), row.names(SCN05Clean), row.names(SCN06Clean), row.names(SCN07Clean), row.names(SCN08Clean), row.names(SCN09Clean), row.names(SCN10Clean), row.names(SCN11Clean), row.names(SCN12Clean), row.names(SCN13Clean), row.names(SCN14Clean), row.names(SCN15Clean), colnames(SCN16), colnames(SCN17)), invert=T) #CheckInput = SCN01_Extras #CheckUMAP(SCN_Seu) SCN02_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < -10 & SCN_UMAP$UMAP_2 < 8 | row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < -6 & SCN_UMAP$UMAP_2 < 4 | row.names(SCN_UMAP) %in% row.names(SCN02Clean)) SCN03_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 3.5 & SCN_UMAP$UMAP_1 < 12 & SCN_UMAP$UMAP_2 > 7.4 | row.names(SCN_UMAP) %in% row.names(SCN03Clean)) SCN04_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < -2 & SCN_UMAP$UMAP_2 > 2 & ! row.names(SCN_UMAP) %in% row.names(SCN02_Extras) | row.names(SCN_UMAP) %in% row.names(SCN04Clean)) SCN05_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 7 & SCN_UMAP$UMAP_2 > -10 & SCN_UMAP$UMAP_2 < -1 | row.names(SCN_UMAP) %in% row.names(SCN05Clean)) SCN08_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 5 & SCN_UMAP$UMAP_1 < 9 & SCN_UMAP$UMAP_2 > -2 & SCN_UMAP$UMAP_2 < 4 & ! row.names(SCN_UMAP) %in% c( row.names(SCN05_Extras))| row.names(SCN_UMAP) %in% row.names(SCN08Clean)) SCN09_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 0 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 < -6.2 & SCN_UMAP$UMAP_2 > -10 | row.names(SCN_UMAP) %in% row.names(SCN09Clean)) SCN06_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 < 3 & SCN_UMAP$UMAP_2 > -14 & SCN_UMAP$UMAP_2 < -8.5 & ! row.names(SCN_UMAP) %in% row.names(SCN09_Extras) | row.names(SCN_UMAP) %in% row.names(SCN06Clean)) SCN11_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 4 & SCN_UMAP$UMAP_1 < 8 & SCN_UMAP$UMAP_2 > -5 & SCN_UMAP$UMAP_2 < 0 & ! row.names(SCN_UMAP) %in% c(row.names(SCN08_Extras), row.names(SCN05_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN11Clean)) SCN01_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > -2 & SCN_UMAP$UMAP_1 < 7 & SCN_UMAP$UMAP_2 > -8 & SCN_UMAP$UMAP_2 < -2 & ! row.names(SCN_UMAP) %in% c(row.names(SCN05_Extras), row.names(SCN11_Extras), row.names(SCN08_Extras), row.names(SCN09_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN01Clean)) SCN10_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > -5 & SCN_UMAP$UMAP_1 < 0 & SCN_UMAP$UMAP_2 < 0 & ! row.names(SCN_UMAP) %in% c(row.names(SCN01_Extras), row.names(SCN06_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN10Clean)) SCN07_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > -4 & SCN_UMAP$UMAP_1 < 7 & SCN_UMAP$UMAP_2 > -5 & SCN_UMAP$UMAP_2 < 5 & ! row.names(SCN_UMAP) %in% c(row.names(SCN01_Extras), row.names(SCN08_Extras), row.names(SCN11_Extras), row.names(SCN10_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN07Clean)) SCN12_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_1 < 14.5 & SCN_UMAP$UMAP_2 < 4.5 & SCN_UMAP$UMAP_2 > -1 | row.names(SCN_UMAP) %in% row.names(SCN12Clean)) SCN13_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 0 & SCN_UMAP$UMAP_1 < 6 & SCN_UMAP$UMAP_2 > 8 & ! row.names(SCN_UMAP) %in% row.names(SCN03_Extras) | row.names(SCN_UMAP) %in% row.names(SCN13Clean)) SCN14_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_1 < 13 & SCN_UMAP$UMAP_2 > 4.5 & SCN_UMAP$UMAP_2 < 9 & ! row.names(SCN_UMAP) %in% row.names(SCN03_Extras) | row.names(SCN_UMAP) %in% row.names(SCN14Clean)) SCN15_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 10 & SCN_UMAP$UMAP_2 > 3 & SCN_UMAP$UMAP_2 < 9 & ! row.names(SCN_UMAP) %in% c(row.names(SCN14_Extras), row.names(SCN12_Extras), row.names(SCN03_Extras)) | row.names(SCN_UMAP) %in% row.names(SCN15Clean)) SCN16_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_2 < -12 & SCN_UMAP$UMAP_1 < 5 & ! row.names(SCN_UMAP) %in% row.names(SCN06_Extras) | row.names(SCN_UMAP) %in% colnames(SCN16)) SCN17_Extras = subset(SCN_UMAP, row.names(SCN_UMAP) %in% colnames(SCN_REST) & SCN_UMAP$UMAP_1 > 5 & SCN_UMAP$UMAP_2 < -11 | row.names(SCN_UMAP) %in% colnames(SCN17)) SCN_Assigns = GenerateMetaData_Barcodes(list( "SCN_01" = SCN01_Extras, "SCN_02" = SCN02_Extras, "SCN_03" = SCN03_Extras, "SCN_04" = SCN04_Extras, "SCN_05" = SCN05_Extras, "SCN_06" = SCN06_Extras, "SCN_07" = SCN07_Extras, "SCN_08" = SCN08_Extras, "SCN_09" = SCN09_Extras, "SCN_10" = SCN10_Extras, "SCN_11" = SCN11_Extras, "SCN_12" = SCN12_Extras, "SCN_13" = SCN13_Extras, "SCN_14" = SCN14_Extras, "SCN_15" = SCN15_Extras, "SCN_16" = SCN16_Extras, "SCN_17" = SCN17_Extras)) SCN_REST2 = subset(SCN_Seu, cells = SCN_Assigns$Barcodes, invert=T) SCN_Assigns$Dups = duplicated(SCN_Assigns$Barcodes) | duplicated(SCN_Assigns$Barcodes, fromLast=T) SCN_Assigns_T = subset(SCN_Assigns, SCN_Assigns$Dups == T) unique(SCN_Assigns_T$Pop) SCN_Assigns = GenerateMetaData(list( "SCN_01" = SCN01_Extras, "SCN_02" = SCN02_Extras, "SCN_03" = SCN03_Extras, "SCN_04" = SCN04_Extras, "SCN_05" = SCN05_Extras, "SCN_06" = SCN06_Extras, "SCN_07" = SCN07_Extras, "SCN_08" = SCN08_Extras, "SCN_09" = SCN09_Extras, "SCN_10" = SCN10_Extras, "SCN_11" = SCN11_Extras, "SCN_12" = SCN12_Extras, "SCN_13" = SCN13_Extras, "SCN_14" = SCN14_Extras, "SCN_15" = SCN15_Extras, "SCN_16" = SCN16_Extras, "SCN_17" = SCN17_Extras, "SCN_18" = SCN_REST2)) CheckInput = SCN03_Extras CheckUMAP(SCN_Seu) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
DMH
GetDMHBarcs = subset(MainAssign, MainAssign$Nuclei == "DMH") DMH_Seu = subset(EdKaZhouHypoNeurons, cells = GetDMHBarcs$Barcs) DefaultAssay(DMH_Seu) = "integrated" DMH_Seu = FindVariableFeatures(DMH_Seu) DMH_Seu = ScaleData(DMH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(DMH_Seu) = "integrated" DMH_Seu = RunPCA(DMH_Seu, npcs = 20) DMH_Seu <- RunUMAP(DMH_Seu, dims = 1:20, spread= 5) DefaultAssay(DMH_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(20,50) #ClusterFunc_All_RNA(DMH_Seu) #CheckInput = DMH07Clean #CheckUMAP(DMH_Seu) DefaultAssay(DMH_Seu) = "integrated" DMH_Seu <- FindNeighbors(DMH_Seu, k.param=20, dims=1:20) DMH_Seu <- FindClusters(DMH_Seu, resolution = 1) DefaultAssay(DMH_Seu) = "RNA" DMH_UMAP = as.data.frame(DMH_Seu@reductions$umap@cell.embeddings) DMH01 = subset(DMH_Seu, idents = 0) DMH02 = subset(DMH_Seu, idents = 1) DMH02Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH02) & DMH_UMAP$UMAP_1 > 7 & DMH_UMAP$UMAP_2 < -7 & DMH_UMAP$UMAP_1 < 22) DMH03 = subset(DMH_Seu, idents = 2) DMH03Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH03) & DMH_UMAP$UMAP_1 > -16 & DMH_UMAP$UMAP_1 < -6 & DMH_UMAP$UMAP_2 > 7 ) DMH04 = subset(DMH_Seu, idents = 3) DMH04Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH04) & DMH_UMAP$UMAP_2 > 0) DMH05 = subset(DMH_Seu, idents = 4) DMH05Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH05) & DMH_UMAP$UMAP_2 > -7) DMH06 = subset(DMH_Seu, idents = 5) DMH06Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH06) & DMH_UMAP$UMAP_2 > -23 & DMH_UMAP$UMAP_2 < -11) DMH07 = subset(DMH_Seu, idents = 6) DMH07Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH07) & DMH_UMAP$UMAP_2 > -10 & DMH_UMAP$UMAP_2 < 11) DMH08 = subset(DMH_Seu, idents = 7) DMH08Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH08) & DMH_UMAP$UMAP_2 < 11) DMH09 = subset(DMH_Seu, idents = 8) DMH10 = subset(DMH_Seu, idents = 9) DMH10Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH10) & DMH_UMAP$UMAP_1 < -20) DMH11 = subset(DMH_Seu, idents = 10) DMH11Clean = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH11) & DMH_UMAP$UMAP_2 < -20) DMH12 = subset(DMH_Seu, idents = 11) #CheckInput = DMH04Clean #CheckUMAP(DMH_Seu) DMH_REST = subset(DMH_Seu, cells = c(colnames(DMH01), row.names(DMH02Clean), row.names(DMH03Clean), row.names(DMH04Clean), row.names(DMH05Clean),row.names(DMH06Clean), row.names(DMH07Clean), row.names(DMH08Clean), colnames(DMH09), row.names(DMH10Clean), row.names(DMH11Clean), colnames(DMH12)), invert=T) #CheckInput = DMH07_Extras #CheckUMAP(DMH_Seu) DMH02_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > 5 & DMH_UMAP$UMAP_1 < 18 & DMH_UMAP$UMAP_2 < 0 | row.names(DMH_UMAP) %in% row.names(DMH02Clean)) DMH03_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST)& DMH_UMAP$UMAP_1 > -16 & DMH_UMAP$UMAP_1 < -6 & DMH_UMAP$UMAP_2 > 7 | row.names(DMH_UMAP) %in% row.names(DMH03Clean)) DMH04_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_2 > 3 & DMH_UMAP$UMAP_2 < 15 & DMH_UMAP$UMAP_1 < -10 & ! row.names(DMH_UMAP) %in% row.names(DMH03_Extras) | row.names(DMH_UMAP) %in% row.names(DMH04Clean)) DMH05_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_2 > -10 & DMH_UMAP$UMAP_1 > 20 | row.names(DMH_UMAP) %in% row.names(DMH05Clean)) DMH06_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 5 & DMH_UMAP$UMAP_2 > -22 & DMH_UMAP$UMAP_2 < -12 | row.names(DMH_UMAP) %in% row.names(DMH06Clean)) DMH07_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 10 & DMH_UMAP$UMAP_1 > -8 & DMH_UMAP$UMAP_2 > -1 & DMH_UMAP$UMAP_2 < 9 | row.names(DMH_UMAP) %in% row.names(DMH07Clean)) DMH01_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > -8 & DMH_UMAP$UMAP_2 > 7 & ! row.names(DMH_UMAP) %in% row.names(DMH07_Extras) | row.names(DMH_UMAP) %in% colnames(DMH01)) DMH08_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 1 & DMH_UMAP$UMAP_2 > -9 & DMH_UMAP$UMAP_2 < -1 & ! row.names(DMH_UMAP) %in% row.names(DMH07_Extras) | row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > -9 & DMH_UMAP$UMAP_1 < -7 & DMH_UMAP$UMAP_2 > 0 & DMH_UMAP$UMAP_2 < 2 | row.names(DMH_UMAP) %in% row.names(DMH08Clean)) DMH09_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > 10 & DMH_UMAP$UMAP_2 < -5 & ! row.names(DMH_UMAP) %in% row.names(DMH02_Extras) | row.names(DMH_UMAP) %in% colnames(DMH09)) DMH11_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 < 10 & DMH_UMAP$UMAP_2 < -22 | row.names(DMH_UMAP) %in% row.names(DMH11Clean)) DMH12_Extras = subset(DMH_UMAP, row.names(DMH_UMAP) %in% colnames(DMH_REST) & DMH_UMAP$UMAP_1 > -18 & DMH_UMAP$UMAP_1 < -10 & DMH_UMAP$UMAP_2 > -2 & DMH_UMAP$UMAP_2 < 5 & ! row.names(DMH_UMAP) %in% row.names(DMH04_Extras) | row.names(DMH_UMAP) %in% colnames(DMH12)) DMH_Assigns = GenerateMetaData_Barcodes(list("DMH_01" = DMH01_Extras, "DMH_02" = DMH02_Extras,"DMH_03" = DMH03_Extras, "DMH_04" = DMH04_Extras, "DMH_05" = DMH05_Extras, "DMH_06" = DMH06_Extras, "DMH_07" = DMH07_Extras, "DMH_08" = DMH08_Extras, "DMH_09" = DMH09_Extras, "DMH_10" = DMH10Clean, "DMH_11" = DMH11_Extras, "DMH_12" = DMH12_Extras)) DMH13 = subset(DMH_Seu, cells = DMH_Assigns$Barcodes, invert=T) DMH_Assigns$Dups = duplicated(DMH_Assigns$Barcodes) | duplicated(DMH_Assigns$Barcodes, fromLast=T) DMH_Assigns_T = subset(DMH_Assigns, DMH_Assigns$Dups == T) unique(DMH_Assigns_T$Pop) DMH_Assigns = GenerateMetaData(list("DMH_01" = DMH01_Extras, "DMH_02" = DMH02_Extras,"DMH_03" = DMH03_Extras, "DMH_04" = DMH04_Extras, "DMH_05" = DMH05_Extras, "DMH_06" = DMH06_Extras, "DMH_07" = DMH07_Extras, "DMH_08" = DMH08_Extras, "DMH_09" = DMH09_Extras, "DMH_10" = DMH10Clean, "DMH_11" = DMH11_Extras, "DMH_12" = DMH12_Extras, "DMH_13" = DMH13)) #CheckInput = DMH_Assigns #CheckUMAP(DMH_Seu) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
PO
GetPOBarcs = subset(MainAssign, MainAssign$Nuclei == "PO") PO_Seu = subset(EdKaZhouHypoNeurons, cells = GetPOBarcs$Barcs) DefaultAssay(PO_Seu) = "integrated" PO_Seu = FindVariableFeatures(PO_Seu) PO_Seu = ScaleData(PO_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(PO_Seu) = "integrated" PO_Seu = RunPCA(PO_Seu, npcs = 20) PO_Seu <- RunUMAP(PO_Seu, dims = 1:20, spread= 5) DefaultAssay(PO_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(30, 40) #ClusterFunc_All_RNA(PO_Seu) #CheckInput = PO10Clean #CheckUMAP(PO_Seu) DefaultAssay(PO_Seu) = "integrated" PO_Seu <- FindNeighbors(PO_Seu, k.param=20, dims=1:20) PO_Seu <- FindClusters(PO_Seu, resolution = 1) DefaultAssay(PO_Seu) = "RNA" PO_UMAP = as.data.frame(PO_Seu@reductions$umap@cell.embeddings) PO01 = subset(PO_Seu, idents = c(0, 12)) PO01Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO01) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 < -6 ) PO02 = subset(PO_Seu, idents = c(1,8)) PO02Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO02) & PO_UMAP$UMAP_1 > 2 & PO_UMAP$UMAP_2 < 16 & PO_UMAP$UMAP_2 > 5 | row.names(PO_UMAP) %in% colnames(PO02) & PO_UMAP$UMAP_1 > 6 & PO_UMAP$UMAP_2 < 5 & PO_UMAP$UMAP_2 > -4 ) PO03 = subset(PO_Seu, idents = 2) PO03Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO03) & PO_UMAP$UMAP_2 > 5 ) PO04 = subset(PO_Seu, idents = c(3,4)) PO04Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO04) &PO_UMAP$UMAP_2 < -5.5 & PO_UMAP$UMAP_1 > 0) PO05 = subset(PO_Seu, idents = 5) PO05Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO05) & PO_UMAP$UMAP_2 > 10 & PO_UMAP$UMAP_1 > 0 & PO_UMAP$UMAP_1 < 12) PO06 = subset(PO_Seu, idents = 6) PO06Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO06) & PO_UMAP$UMAP_1 < 1& PO_UMAP$UMAP_2 > -5 & PO_UMAP$UMAP_2 < 10) PO07 = subset(PO_Seu, idents = 7) PO07Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO07) & PO_UMAP$UMAP_1 > -10 & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 > -10 & PO_UMAP$UMAP_2 < 5) PO08 = subset(PO_Seu, idents = 9) PO09 = subset(PO_Seu, idents = 10) PO09Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO09) & PO_UMAP$UMAP_1 < 11) PO10 = subset(PO_Seu, idents = 11) PO10Clean = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO10) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_1 > -10) #CheckInput = PO06Clean #CheckUMAP(PO_Seu) PO_REST = subset(PO_Seu, cells = c(row.names(PO01Clean), row.names(PO02Clean), row.names(PO03Clean), row.names(PO04Clean), row.names(PO05Clean),row.names(PO06Clean), row.names(PO07Clean), colnames(PO08), row.names(PO09Clean), row.names(PO10Clean)), invert=T) #CheckInput = PO10_Extras #CheckUMAP(PO_Seu) PO01_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 < -6 | row.names(PO_UMAP) %in% row.names(PO01Clean)) PO02_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 2 & PO_UMAP$UMAP_2 < 13 & PO_UMAP$UMAP_2 > 3.5 | row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 6 & PO_UMAP$UMAP_1 > 6 & PO_UMAP$UMAP_2 < 5 & PO_UMAP$UMAP_2 > -3 | row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 12 & PO_UMAP$UMAP_2 < 19 & PO_UMAP$UMAP_2 > 5 | row.names(PO_UMAP) %in% row.names(PO02Clean)) PO03_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST)& PO_UMAP$UMAP_1 < -10 & PO_UMAP$UMAP_2 > 6 | row.names(PO_UMAP) %in% row.names(PO03Clean)) PO05_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_2 > 13 & PO_UMAP$UMAP_1 > 0 & PO_UMAP$UMAP_1 < 12 | row.names(PO_UMAP) %in% row.names(PO05Clean)) PO06_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > -15 & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 > 2 & PO_UMAP$UMAP_2 < 7 | row.names(PO_UMAP) %in% row.names(PO06Clean)) PO07_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > -10 & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_2 > -10 & PO_UMAP$UMAP_2 < 5 & ! row.names(PO_UMAP) %in% row.names(PO06_Extras)| row.names(PO_UMAP) %in% row.names(PO07Clean)) PO08_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 < -10 & PO_UMAP$UMAP_2 > -8 & PO_UMAP$UMAP_2 < 4 & ! row.names(PO_UMAP) %in% row.names(PO06_Extras) | row.names(PO_UMAP) %in% colnames(PO08)) PO09_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 > 0 & PO_UMAP$UMAP_1 < 10 & PO_UMAP$UMAP_2 > -12 & PO_UMAP$UMAP_2 < 3 & ! row.names(PO_UMAP) %in% row.names(PO02_Extras) | row.names(PO_UMAP) %in% row.names(PO09Clean)) PO04_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_2 < 0 & PO_UMAP$UMAP_1 > 0 & ! row.names(PO_UMAP) %in% c(row.names(PO02_Extras), row.names(PO09_Extras)) | row.names(PO_UMAP) %in% row.names(PO04Clean)) PO10_Extras = subset(PO_UMAP, row.names(PO_UMAP) %in% colnames(PO_REST) & PO_UMAP$UMAP_1 < 0 & PO_UMAP$UMAP_1 > -10 & PO_UMAP$UMAP_2 > 8 & ! row.names(PO_UMAP) %in% c(row.names(PO03_Extras), row.names(PO06_Extras)) | row.names(PO_UMAP) %in% row.names(PO10Clean)) PO_Assigns = GenerateMetaData_Barcodes(list("PO_01" = PO01_Extras, "PO_02" = PO02_Extras,"PO_03" = PO03_Extras, "PO_04" = PO04_Extras, "PO_05" = PO05_Extras, "PO_06" = PO06_Extras, "PO_07" = PO07_Extras, "PO_08" = PO08_Extras, "PO_09" = PO09_Extras, "PO_10" = PO10_Extras)) PO_6pt2 = subset(PO_Seu, cells = PO_Assigns$Barcodes, invert=T) PO_Assigns$Dups = duplicated(PO_Assigns$Barcodes) | duplicated(PO_Assigns$Barcodes, fromLast=T) PO_Assigns_T = subset(PO_Assigns, PO_Assigns$Dups == T) unique(PO_Assigns_T$Pop) PO_Assigns = GenerateMetaData(list("PO_01" = PO01_Extras, "PO_02" = PO02_Extras,"PO_03" = PO03_Extras, "PO_04" = PO04_Extras, "PO_05" = PO05_Extras,"PO_06" = PO06_Extras, "PO_06" = PO_6pt2, "PO_07" = PO07_Extras, "PO_08" = PO08_Extras, "PO_09" = PO09_Extras, "PO_10" = PO10_Extras)) #CheckInput = PO_REST2 #CheckUMAP(PO_Seu) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
AH
GetAHBarcs = subset(MainAssign, MainAssign$Nuclei == "AH") AH_Seu = subset(EdKaZhouHypoNeurons, cells = GetAHBarcs$Barcs) DefaultAssay(AH_Seu) = "integrated" AH_Seu = FindVariableFeatures(AH_Seu) AH_Seu = ScaleData(AH_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(AH_Seu) = "integrated" AH_Seu = RunPCA(AH_Seu, npcs = 20) AH_Seu <- RunHarmony(AH_Seu, group.by.vars = "SampleAdult") AH_Seu <- RunUMAP(AH_Seu, dims = 1:20, spread= 5, reduction = "harmony") DefaultAssay(AH_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(10,100) ##ClusterFunc_All_RNA(AH_Seu) #CheckInput = AH01Clean #CheckUMAP(AH_Seu) DefaultAssay(AH_Seu) = "integrated" AH_Seu <- FindNeighbors(AH_Seu, k.param=20, dims=1:20) AH_Seu <- FindClusters(AH_Seu, resolution = 1) DefaultAssay(AH_Seu) = "RNA" AH_UMAP = as.data.frame(AH_Seu@reductions$umap@cell.embeddings) AH01 = subset(AH_Seu, idents = c(3)) AH01Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH01) & AH_UMAP$UMAP_1 > 10 & AH_UMAP$UMAP_1 < 26 & AH_UMAP$UMAP_2 > -7 ) AH02 = subset(AH_Seu, idents = c(4)) AH02Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH02) & AH_UMAP$UMAP_1 > 0.5 & AH_UMAP$UMAP_2 < -10.5) AH03 = subset(AH_Seu, idents = c(5,18)) AH03Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH03) & AH_UMAP$UMAP_1 > -18 & AH_UMAP$UMAP_2 > 5) AH04 = subset(AH_Seu, idents = c(6)) AH04Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH04) & AH_UMAP$UMAP_2 > 23) AH05 = subset(AH_Seu, idents = 7) AH05Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH05) & AH_UMAP$UMAP_2 < -23 ) AH06 = subset(AH_Seu, idents = c(8,16,9)) AH06Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH06) & AH_UMAP$UMAP_1 > 20) AH07Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH06) & AH_UMAP$UMAP_1 < 20 & AH_UMAP$UMAP_2 < 5) AH08 = subset(AH_Seu, idents = c(10,19)) AH08Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH08) & AH_UMAP$UMAP_1 < -10 & AH_UMAP$UMAP_2 < -10) AH09 = subset(AH_Seu, idents = c(11)) AH09Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH09) & AH_UMAP$UMAP_2 < 24) AH10 = subset(AH_Seu, idents = c(12)) AH11 = subset(AH_Seu, idents = c(13,20)) AH11Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH11) & AH_UMAP$UMAP_1 < -10) AH12 = subset(AH_Seu, idents = c(14)) AH12Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH12) & AH_UMAP$UMAP_2 < -10) AH13 = subset(AH_Seu, idents = c(15,17)) AH14 = subset(AH_Seu, idents = c(21)) AH14Clean = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH14) & AH_UMAP$UMAP_1 > 0 & AH_UMAP$UMAP_1 < 15) AH15 = subset(AH_Seu, idents = c(22)) #CheckInput = AH12Clean #CheckUMAP(AH_Seu) AH_REST = subset(AH_Seu, cells = c(row.names(AH01Clean), row.names(AH02Clean), row.names(AH03Clean), row.names(AH04Clean), row.names(AH05Clean),row.names(AH06Clean), row.names(AH07Clean), row.names(AH08Clean), row.names(AH09Clean), row.names(AH11Clean), row.names(AH12Clean), colnames(AH10), colnames(AH13),row.names(AH14Clean), colnames(AH15)), invert=T) #CheckInput = AH08Clean #CheckUMAP(AH_Seu) AH01_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < 25 & AH_UMAP$UMAP_1 > 11 & AH_UMAP$UMAP_2 > -7.2 & AH_UMAP$UMAP_2 < 20 | row.names(AH_UMAP) %in% row.names(AH01Clean)) AH02_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 2 & AH_UMAP$UMAP_1 < 10 & AH_UMAP$UMAP_2 > -16 & AH_UMAP$UMAP_2 < -10 | row.names(AH_UMAP) %in% row.names(AH02Clean)) AH03_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -19 & AH_UMAP$UMAP_1 < -10 & AH_UMAP$UMAP_2 > 7 & AH_UMAP$UMAP_2 < 18 | row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -12 & AH_UMAP$UMAP_1 < 0 & AH_UMAP$UMAP_2 > 15 | row.names(AH_UMAP) %in% row.names(AH03Clean)) AH04_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 0 & AH_UMAP$UMAP_2 > 23 | row.names(AH_UMAP) %in% row.names(AH04Clean)) AH05_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_2 < -21 & AH_UMAP$UMAP_1 > -10 & AH_UMAP$UMAP_1 < 8 | row.names(AH_UMAP) %in% row.names(AH05Clean)) AH06_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 23 & AH_UMAP$UMAP_2 > -10 & AH_UMAP$UMAP_2 < 10 & ! row.names(AH_UMAP) %in% row.names(AH01_Extras)| row.names(AH_UMAP) %in% row.names(AH06Clean)) AH07_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 0 & AH_UMAP$UMAP_1 < 11 & AH_UMAP$UMAP_2 > -3 & AH_UMAP$UMAP_2 < 5 | row.names(AH_UMAP) %in% row.names(AH07Clean)) AH08_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < -18 & AH_UMAP$UMAP_2 < -5 | row.names(AH_UMAP) %in% row.names(AH08Clean)) AH09_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -2 & AH_UMAP$UMAP_1 < 12 & AH_UMAP$UMAP_2 > 5 & ! row.names(AH_UMAP) %in% row.names(AH04_Extras) | row.names(AH_UMAP) %in% row.names(AH09Clean)) AH10_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 12 & AH_UMAP$UMAP_1 < 25 & AH_UMAP$UMAP_2 > -18 & AH_UMAP$UMAP_2 < -7 & ! row.names(AH_UMAP) %in% row.names(AH01_Extras) | row.names(AH_UMAP) %in% colnames(AH10)) AH11_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < -22 & AH_UMAP$UMAP_2 > 13 | row.names(AH_UMAP) %in% row.names(AH11Clean)) AH12_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 9 & AH_UMAP$UMAP_2 < -20 | row.names(AH_UMAP) %in% row.names(AH12Clean)) AH13_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 < -25 & AH_UMAP$UMAP_2 > -5 & AH_UMAP$UMAP_2 < 15 | row.names(AH_UMAP) %in% colnames(AH13)) AH14_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > 3 & AH_UMAP$UMAP_1 < 13 & AH_UMAP$UMAP_2 > -22 & AH_UMAP$UMAP_2 < -15 | row.names(AH_UMAP) %in% row.names(AH14Clean)) AH15_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST) & AH_UMAP$UMAP_1 > -15 & AH_UMAP$UMAP_1 < -8 & AH_UMAP$UMAP_2 > -10 & AH_UMAP$UMAP_2 < -3 | row.names(AH_UMAP) %in% colnames(AH15)) #CheckInput = AH17_Extras #CheckUMAP(AH_Seu) AH_REST2 = subset(AH_Seu, cells = c(row.names(AH01_Extras), row.names(AH02_Extras), row.names(AH03_Extras), row.names(AH04_Extras), row.names(AH05_Extras),row.names(AH06_Extras), row.names(AH07_Extras), row.names(AH08_Extras), row.names(AH09_Extras), row.names(AH11_Extras), row.names(AH12_Extras), row.names(AH10_Extras), row.names(AH13_Extras), row.names(AH14_Extras), row.names(AH15_Extras)), invert=T) AH16_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST2) & AH_UMAP$UMAP_2 < 10) AH17_Extras = subset(AH_UMAP, row.names(AH_UMAP) %in% colnames(AH_REST2) & AH_UMAP$UMAP_2 > 10) AH_Assigns = GenerateMetaData_Barcodes(list("AH_01" = AH01_Extras, "AH_02" = AH02_Extras,"AH_03" = AH03_Extras, "AH_04" = AH04_Extras, "AH_05" = AH05_Extras, "AH_06" = AH06_Extras, "AH_07" = AH07_Extras, "AH_08" = AH08_Extras, "AH_09" = AH09_Extras, "AH_10" = AH10_Extras, "AH_11" = AH11_Extras, "AH_12" = AH12_Extras,"AH_13" = AH13_Extras, "AH_14" = AH14_Extras, "AH_15" = AH15_Extras)) #AH_01_Rest = subset(AH_Seu, cells = AH_Assigns$Barcodes, invert=T) AH_Assigns$Dups = duplicated(AH_Assigns$Barcodes) | duplicated(AH_Assigns$Barcodes, fromLast=T) AH_Assigns_T = subset(AH_Assigns, AH_Assigns$Dups == T) unique(AH_Assigns_T$Pop) AH_Assigns = GenerateMetaData(list("AH_01" = AH01_Extras, "AH_02" = AH02_Extras,"AH_03" = AH03_Extras, "AH_04" = AH04_Extras, "AH_05" = AH05_Extras, "AH_06" = AH06_Extras, "AH_07" = AH07_Extras, "AH_08" = AH08_Extras, "AH_09" = AH09_Extras, "AH_10" = AH10_Extras, "AH_11" = AH11_Extras, "AH_12" = AH12_Extras,"AH_13" = AH13_Extras, "AH_14" = AH14_Extras, "AH_15" = AH15_Extras, "AH_16" = AH16_Extras, "AH_17" = AH17_Extras)) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns", "AH_Seu", "AH_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
MN
GetMNBarcs = subset(MainAssign, MainAssign$Nuclei == "MN") MN_Seu = subset(EdKaZhouHypoNeurons, cells = GetMNBarcs$Barcs) DefaultAssay(MN_Seu) = "integrated" MN_Seu = FindVariableFeatures(MN_Seu) MN_Seu = ScaleData(MN_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(MN_Seu) = "integrated" MN_Seu = RunPCA(MN_Seu, npcs = 20) MN_Seu <- RunHarmony(MN_Seu, group.by.vars = "SampleAdult") MN_Seu <- RunUMAP(MN_Seu, dims = 1:20, spread= 5, reduction="harmony") DefaultAssay(MN_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(50, 100) #ClusterFunc_All_RNA(MN_Seu) DefaultAssay(MN_Seu) = "integrated" MN_Seu <- FindNeighbors(MN_Seu, k.param=50, dims=1:20) MN_Seu <- FindClusters(MN_Seu, resolution = 1) DefaultAssay(MN_Seu) = "RNA" MN_UMAP = as.data.frame(MN_Seu@reductions$umap@cell.embeddings) MN01 = subset(MN_Seu, idents = c(0)) MN01Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN01) & MN_UMAP$UMAP_1 < -17 & MN_UMAP$UMAP_2 > -17 & MN_UMAP$UMAP_2 < 11) MN02 = subset(MN_Seu, idents = c(1,3)) MN02Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN02) & MN_UMAP$UMAP_1 > -14 & MN_UMAP$UMAP_1 < 0 & MN_UMAP$UMAP_2 > 4) MN22Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN02) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < 10 & MN_UMAP$UMAP_2 > 0) MN23Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN02) & MN_UMAP$UMAP_1 > -3 & MN_UMAP$UMAP_1 < 6 & MN_UMAP$UMAP_2 > -5 & MN_UMAP$UMAP_2 < 4) #CheckInput = MN06Clean #CheckUMAP(MN_Seu) MN03 = subset(MN_Seu, idents = 2) MN03Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN03) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 10.5 & MN_UMAP$UMAP_2 > -3 & MN_UMAP$UMAP_2 < 10) MN22_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN03) & MN_UMAP$UMAP_1 > 10.5 & MN_UMAP$UMAP_1 < 17 & MN_UMAP$UMAP_2 > -3 & MN_UMAP$UMAP_2 < 10) MN04 = subset(MN_Seu, idents = c(4)) MN04Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN04) & MN_UMAP$UMAP_2 > 10) MN05 = subset(MN_Seu, idents = 6) MN05Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN05) & MN_UMAP$UMAP_1 > -16 & MN_UMAP$UMAP_1 < 0 & MN_UMAP$UMAP_2 < -15) MN24Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN05) & MN_UMAP$UMAP_2 > -15 & MN_UMAP$UMAP_2 < -9 & MN_UMAP$UMAP_1 < -12) MN06 = subset(MN_Seu, idents = c(7, 9)) MN06Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN06) & MN_UMAP$UMAP_1 > 22 & MN_UMAP$UMAP_2 > -14 | row.names(MN_UMAP) %in% colnames(MN06) & MN_UMAP$UMAP_1 > 21 & MN_UMAP$UMAP_2 < -1 & MN_UMAP$UMAP_2 > -15 | row.names(MN_UMAP) %in% colnames(MN06) & MN_UMAP$UMAP_1 > 17 & MN_UMAP$UMAP_2 < -9 & MN_UMAP$UMAP_2 > -14) MN07 = subset(MN_Seu, idents = 8) MN07Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN07) & MN_UMAP$UMAP_1 > -12 & MN_UMAP$UMAP_1 < -4 & MN_UMAP$UMAP_2 > -9 & MN_UMAP$UMAP_2 < 1 ) MN25Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN07) & MN_UMAP$UMAP_1 > -18 & MN_UMAP$UMAP_1 < -12 & MN_UMAP$UMAP_2 > -11 & MN_UMAP$UMAP_2 < 3) MN08 = subset(MN_Seu, idents = 10) MN08Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN08) & MN_UMAP$UMAP_1 > -3 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 > 10) MN26Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN08) & MN_UMAP$UMAP_1 > 14 & MN_UMAP$UMAP_2 > -4 & MN_UMAP$UMAP_2 <10) MN09 = subset(MN_Seu, idents = c(11)) MN09Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN09) & MN_UMAP$UMAP_1 > 7 & MN_UMAP$UMAP_1 < 14) MN10Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN09) & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_1 > 1 & MN_UMAP$UMAP_2 > -15 & MN_UMAP$UMAP_2 < -10) MN11Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN09) & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -3) MN12 = subset(MN_Seu, idents = c(12)) MN12Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN12) & MN_UMAP$UMAP_1 > -17 & MN_UMAP$UMAP_1 < -7 & MN_UMAP$UMAP_2 > -8) MN13 = subset(MN_Seu, idents = c(13)) MN13Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN13) & MN_UMAP$UMAP_1 > -5 & MN_UMAP$UMAP_1 < 3 &MN_UMAP$UMAP_2 < -15) MN14Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN13) & MN_UMAP$UMAP_1 > 11 & MN_UMAP$UMAP_1 < 17 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < 0) MN15 = subset(MN_Seu, idents = c(14)) MN15Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN15) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < -13) MN16 = subset(MN_Seu, idents = c(15)) MN16Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN16) & MN_UMAP$UMAP_1 > -22 & MN_UMAP$UMAP_1 < -10 & MN_UMAP$UMAP_2 < 8) MN17 = subset(MN_Seu, idents = c(16)) MN17Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN17) & MN_UMAP$UMAP_1 > 11) MN18 = subset(MN_Seu, idents = c(17)) MN18Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN18) & MN_UMAP$UMAP_1 > -11 & MN_UMAP$UMAP_1 < 1 & MN_UMAP$UMAP_2 > -18 & MN_UMAP$UMAP_2 < -9) MN19 = subset(MN_Seu, idents = c(18)) MN19Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN19) & MN_UMAP$UMAP_1 > 1 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -1) MN20 = subset(MN_Seu, idents = c(19)) MN20Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN20) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_1 < 22) MN21 = subset(MN_Seu, idents = c(20)) MN21Clean = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN21) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 < -15) MN_REST = subset(MN_Seu, cells = c(row.names(MN01Clean), row.names(MN02Clean), row.names(MN03Clean), row.names(MN04Clean), row.names(MN05Clean), row.names(MN06Clean), row.names(MN07Clean), row.names(MN08Clean), row.names(MN09Clean), row.names(MN10Clean), row.names(MN11Clean), row.names(MN12Clean), row.names(MN13Clean), row.names(MN14Clean), row.names(MN15Clean), row.names(MN16Clean), row.names(MN17Clean), row.names(MN18Clean), row.names(MN19Clean), row.names(MN20Clean), row.names(MN21Clean), row.names(MN22_Pt2), row.names(MN22Clean), row.names(MN23Clean), row.names(MN24Clean), row.names(MN25Clean), row.names(MN26Clean)), invert=T) MN02_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST)& MN_UMAP$UMAP_1 > -12 & MN_UMAP$UMAP_1 < -3 & MN_UMAP$UMAP_2 > 3 | row.names(MN_UMAP) %in% row.names(MN02Clean)) MN03_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 2 & MN_UMAP$UMAP_1 < 9 & MN_UMAP$UMAP_2 > -1 & MN_UMAP$UMAP_2 < 12 | row.names(MN_UMAP) %in% row.names(MN03Clean)) MN04_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 > 10 | row.names(MN_UMAP) %in% row.names(MN04Clean)) MN06_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 21.5 & MN_UMAP$UMAP_2 > -12 & MN_UMAP$UMAP_2 < 5 | row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 20.6 & MN_UMAP$UMAP_2 > -12 & MN_UMAP$UMAP_2 < 0 | row.names(MN_UMAP) %in% row.names(MN06Clean)) MN07_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -12 & MN_UMAP$UMAP_1 < -5 & MN_UMAP$UMAP_2 > -9 & MN_UMAP$UMAP_2 < -1 | row.names(MN_UMAP) %in% row.names(MN07Clean)) MN08_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -8 & MN_UMAP$UMAP_1 < 8 & MN_UMAP$UMAP_2 > 12 & ! row.names(MN_UMAP) %in% row.names(MN02_Extras) | row.names(MN_UMAP) %in% row.names(MN08Clean)) MN09_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 7 & MN_UMAP$UMAP_1 < 13 & MN_UMAP$UMAP_2 > -13 & MN_UMAP$UMAP_2 < -5 | row.names(MN_UMAP) %in% row.names(MN09Clean)) MN10_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_2 > -16 & MN_UMAP$UMAP_2 < -9 | row.names(MN_UMAP) %in% row.names(MN10Clean)) MN11_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -1 & MN_UMAP$UMAP_1 < 8 & MN_UMAP$UMAP_2 > -9 & MN_UMAP$UMAP_2 < -5 & ! row.names(MN_UMAP) %in% row.names(MN09_Extras)| row.names(MN_UMAP) %in% c(row.names(MN11Clean), row.names(MN19Clean))) MN13_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -4 & MN_UMAP$UMAP_1 < 1 & MN_UMAP$UMAP_2 > -21 & MN_UMAP$UMAP_2 < -16 | row.names(MN_UMAP) %in% row.names(MN13Clean)) MN05_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -17 & MN_UMAP$UMAP_1 < -3 & MN_UMAP$UMAP_2 < -16 & ! row.names(MN_UMAP) %in% row.names(MN13_Extras)| row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -3 & MN_UMAP$UMAP_1 < 1 & MN_UMAP$UMAP_2 < -23 & ! row.names(MN_UMAP) %in% row.names(MN13_Extras)| row.names(MN_UMAP) %in% row.names(MN05Clean)) MN14_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_1 < 17 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < 0 & ! row.names(MN_UMAP) %in% row.names(MN09_Extras)| MN_UMAP$UMAP_1 > 15 & MN_UMAP$UMAP_1 < 18 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -8 | row.names(MN_UMAP) %in% row.names(MN14Clean)) MN15_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 14 & MN_UMAP$UMAP_2 < -21 | row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 20 & MN_UMAP$UMAP_2 < -12| row.names(MN_UMAP) %in% row.names(MN15Clean)) MN16_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -20 & MN_UMAP$UMAP_1 < -13 & MN_UMAP$UMAP_2 > -5 & MN_UMAP$UMAP_2 < 5 | row.names(MN_UMAP) %in% row.names(MN16Clean)) MN01_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 < -20 | row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 < -18 & MN_UMAP$UMAP_2 < -3 & MN_UMAP$UMAP_2 > -10 & ! row.names(MN_UMAP) %in% row.names(MN16_Extras) | row.names(MN_UMAP) %in% row.names(MN01Clean)) MN12_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -15 & MN_UMAP$UMAP_1 < -7 & MN_UMAP$UMAP_2 > -6 & ! row.names(MN_UMAP) %in% c(row.names(MN02_Extras), row.names(MN07_Extras), row.names(MN16_Extras)) | row.names(MN_UMAP) %in% row.names(MN12Clean)) MN17_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < -12 & ! row.names(MN_UMAP) %in% c(row.names(MN15_Extras), row.names(MN06_Extras))| row.names(MN_UMAP) %in% row.names(MN17Clean)) MN18_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 2 & MN_UMAP$UMAP_2 > -18 & MN_UMAP$UMAP_2 < -9 & ! row.names(MN_UMAP) %in% c(row.names(MN05_Extras), row.names(MN13_Extras))| row.names(MN_UMAP) %in% row.names(MN18Clean)) MN10_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 7 & MN_UMAP$UMAP_2 > -16 & MN_UMAP$UMAP_2 < -9 & ! row.names(MN_UMAP) %in% row.names(MN18_Extras) | row.names(MN_UMAP) %in% row.names(MN10Clean)) MN19_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 16 & MN_UMAP$UMAP_1 < 24 & MN_UMAP$UMAP_2 > -1 & MN_UMAP$UMAP_2 < 6 & ! row.names(MN_UMAP) %in% row.names(MN05_Extras)| row.names(MN_UMAP) %in% row.names(MN26Clean)) MN20_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 15 & MN_UMAP$UMAP_1 < 22 & MN_UMAP$UMAP_2 > -11 & MN_UMAP$UMAP_2 < 0 & ! row.names(MN_UMAP) %in% c(row.names(MN06_Extras), row.names(MN14_Extras), row.names(MN19_Extras))| row.names(MN_UMAP) %in% row.names(MN20Clean)) MN21_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 0 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 < -18 & ! row.names(MN_UMAP) %in% c(row.names(MN05_Extras), row.names(MN13_Extras))| row.names(MN_UMAP) %in% row.names(MN21Clean)) MN22_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > 8 & MN_UMAP$UMAP_1 < 22 & MN_UMAP$UMAP_2 > -1 & MN_UMAP$UMAP_2 < 10 & ! row.names(MN_UMAP) %in% c(row.names(MN19_Extras), row.names(MN03_Extras))| row.names(MN_UMAP) %in% c(row.names(MN22Clean), row.names(MN22_Pt2))) MN23_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -4 & MN_UMAP$UMAP_1 < 5 & MN_UMAP$UMAP_2 > -5 & MN_UMAP$UMAP_2 < 4 & ! row.names(MN_UMAP) %in% c(row.names(MN03_Extras), row.names(MN11_Extras))| row.names(MN_UMAP) %in% row.names(MN23Clean)) MN24_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 < -11 & MN_UMAP$UMAP_2 > -17 & MN_UMAP$UMAP_2 < -10 & ! row.names(MN_UMAP) %in% c(row.names(MN05_Extras), row.names(MN01_Extras))| row.names(MN_UMAP) %in% row.names(MN24Clean)) MN25_Extras = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_REST) & MN_UMAP$UMAP_1 > -19 & MN_UMAP$UMAP_1 < -10 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < -4 & ! row.names(MN_UMAP) %in% c(row.names(MN05Clean), row.names(MN07_Extras), row.names(MN01_Extras), row.names(MN16_Extras), row.names(MN12_Extras))| row.names(MN_UMAP) %in% row.names(MN25Clean)) #CheckInput = MN_Rest2 #CheckUMAP(MN_Seu) ############# MN_Assigns_V1 = GenerateMetaData_Barcodes(list("MN_01" = MN01_Extras, "MN_02" = MN02_Extras,"MN_03" = MN03_Extras, "MN_04" = MN04_Extras, "MN_05" = MN05_Extras, "MN_06" = MN06_Extras, "MN_07" = MN07_Extras, "MN_08" = MN08_Extras, "MN_09" = MN09_Extras, "MN_10" = MN10_Extras, "MN_11" = MN11_Extras, "MN_12" = MN12_Extras,"MN_13" = MN13_Extras, "MN_14" = MN14_Extras, "MN_15" = MN15_Extras, "MN_16" = MN16_Extras, "MN_17" = MN17_Extras, "MN_18" = MN18_Extras, "MN_19" = MN19_Extras,"MN_20" = MN20_Extras, "MN_21" = MN21_Extras, "MN_22" = MN22_Extras,"MN_23" = MN23_Extras, "MN_24" = MN24_Extras, "MN_25" = MN25_Extras)) MN_Rest2 = subset(MN_Seu, cells = MN_Assigns_V1$Barcodes, invert=T) MN01_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 < -10 & MN_UMAP$UMAP_2 > 10 | row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -8 & MN_UMAP$UMAP_1 < -1 & MN_UMAP$UMAP_2 > 3) MN05_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2)& MN_UMAP$UMAP_1 < 3 & MN_UMAP$UMAP_2 < -20) MN08_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 5 & MN_UMAP$UMAP_2 > 8 & ! row.names(MN_UMAP) %in% row.names(MN01_Pt2)) MN11_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 10 & MN_UMAP$UMAP_2 > -10 & MN_UMAP$UMAP_2 < 0) MN12_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 < -12 & MN_UMAP$UMAP_2 > 0 & ! row.names(MN_UMAP) %in% row.names(MN01_Pt2)) MN17_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > 10 & MN_UMAP$UMAP_2 < -10) MN23_Pt2 = subset(MN_UMAP, row.names(MN_UMAP) %in% colnames(MN_Rest2) & MN_UMAP$UMAP_1 > -10 & MN_UMAP$UMAP_1 < 5 & MN_UMAP$UMAP_2 > -2 & MN_UMAP$UMAP_2 < 10 & ! row.names(MN_UMAP) %in% c(row.names(MN01_Pt2), row.names(MN08_Pt2), row.names(MN11_Pt2))) MN_Assigns = GenerateMetaData(list("MN_01" = MN01_Extras, "MN_02" = MN02_Extras,"MN_03" = MN03_Extras, "MN_04" = MN04_Extras, "MN_05" = MN05_Extras, "MN_06" = MN06_Extras, "MN_07" = MN07_Extras, "MN_08" = MN08_Extras, "MN_09" = MN09_Extras, "MN_10" = MN10_Extras, "MN_11" = MN11_Extras, "MN_12" = MN12_Extras,"MN_13" = MN13_Extras, "MN_14" = MN14_Extras, "MN_15" = MN15_Extras, "MN_16" = MN16_Extras, "MN_17" = MN17_Extras, "MN_18" = MN18_Extras, "MN_19" = MN19_Extras,"MN_20" = MN20_Extras, "MN_21" = MN21_Extras, "MN_22" = MN22_Extras,"MN_23" = MN23_Extras, "MN_24" = MN24_Extras, "MN_25" = MN25_Extras, "MN_01" = MN01_Pt2, "MN_05" = MN05_Pt2, "MN_08" = MN08_Pt2, "MN_11" = MN11_Pt2, "MN_17" = MN17_Pt2, "MN_12" = MN12_Pt2, "MN_23" = MN23_Pt2)) #MN_Assigns$Dups = duplicated(MN_Assigns$Barcodes) | duplicated(MN_Assigns$Barcodes, fromLast=T) #MN_Assigns_T = subset(MN_Assigns, MN_Assigns$Dups == T) #unique(MN_Assigns_T$Pop) #CheckInput = MN12_Pt2 #CheckUMAP(MN_Seu) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns", "AH_Seu", "AH_Assigns", "MN_Seu", "MN_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
UNASSIGNED
GetUNASSIGNEDBarcs = subset(MainAssign, MainAssign$Nuclei == "NA") UNASSIGNED_Seu = subset(EdKaZhouHypoNeurons, cells = GetUNASSIGNEDBarcs$Barcs) DefaultAssay(UNASSIGNED_Seu) = "integrated" UNASSIGNED_Seu = FindVariableFeatures(UNASSIGNED_Seu) UNASSIGNED_Seu = ScaleData(UNASSIGNED_Seu, vars.to.regress = c("nFeature_RNA", "percent.mt"), verbose = F) DefaultAssay(UNASSIGNED_Seu) = "integrated" UNASSIGNED_Seu = RunPCA(UNASSIGNED_Seu, npcs = 20) UNASSIGNED_Seu <- RunUMAP(UNASSIGNED_Seu, dims = 1:20, spread= 5) DefaultAssay(UNASSIGNED_Seu) = "RNA" set.dim = 20 set.res = 1 set.kparam = c(50, 100) #ClusterFunc_All_RNA(UNASSIGNED_Seu) #CheckInput = UNASSIGNED01Clean #CheckUMAP(UNASSIGNED_Seu) DefaultAssay(UNASSIGNED_Seu) = "integrated" UNASSIGNED_Seu <- FindNeighbors(UNASSIGNED_Seu, k.param=50, dims=1:20) UNASSIGNED_Seu <- FindClusters(UNASSIGNED_Seu, resolution = 1) DefaultAssay(UNASSIGNED_Seu) = "RNA" UNASSIGNED_UMAP = as.data.frame(UNASSIGNED_Seu@reductions$umap@cell.embeddings) UNASSIGNED01 = subset(UNASSIGNED_Seu, idents = c(0)) UNASSIGNED01Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED01) & UNASSIGNED_UMAP$UMAP_1 > -5 & UNASSIGNED_UMAP$UMAP_1 < 14.7 & UNASSIGNED_UMAP$UMAP_2 > -20 & UNASSIGNED_UMAP$UMAP_2 < 2 ) UNASSIGNED02 = subset(UNASSIGNED_Seu, idents = c(1)) UNASSIGNED02Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED02) & UNASSIGNED_UMAP$UMAP_1 < -10 & UNASSIGNED_UMAP$UMAP_2 > 3) UNASSIGNED03 = subset(UNASSIGNED_Seu, idents = 2) UNASSIGNED03Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED03) & UNASSIGNED_UMAP$UMAP_1 > -13 & UNASSIGNED_UMAP$UMAP_1 < 2 & UNASSIGNED_UMAP$UMAP_2 > -3 & UNASSIGNED_UMAP$UMAP_2 < 11) UNASSIGNED04 = subset(UNASSIGNED_Seu, idents = c(3)) UNASSIGNED04Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED04) & UNASSIGNED_UMAP$UMAP_1 > -16 & UNASSIGNED_UMAP$UMAP_1 < -1 & UNASSIGNED_UMAP$UMAP_2 > -8 & UNASSIGNED_UMAP$UMAP_2 < 4) UNASSIGNED05 = subset(UNASSIGNED_Seu, idents = 4) UNASSIGNED05Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED05) & UNASSIGNED_UMAP$UMAP_1 > 3 & UNASSIGNED_UMAP$UMAP_1 < 15 & UNASSIGNED_UMAP$UMAP_2 > -2 & UNASSIGNED_UMAP$UMAP_2 < 11) UNASSIGNED06 = subset(UNASSIGNED_Seu, idents = 5) UNASSIGNED06Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED06) & UNASSIGNED_UMAP$UMAP_2 < -10) UNASSIGNED07 = subset(UNASSIGNED_Seu, idents = 6) UNASSIGNED07Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED07) & UNASSIGNED_UMAP$UMAP_1 > 5 & UNASSIGNED_UMAP$UMAP_1 < 16 & UNASSIGNED_UMAP$UMAP_2 > 9) UNASSIGNED08 = subset(UNASSIGNED_Seu, idents = 7) UNASSIGNED08Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED08) & UNASSIGNED_UMAP$UMAP_1 > 21.5 & UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < 2) UNASSIGNED09 = subset(UNASSIGNED_Seu, idents = c(8)) UNASSIGNED09Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED09) & UNASSIGNED_UMAP$UMAP_1 < -5 & UNASSIGNED_UMAP$UMAP_2 > 0 & UNASSIGNED_UMAP$UMAP_2 < 14) UNASSIGNED10 = subset(UNASSIGNED_Seu, idents = c(9)) UNASSIGNED10Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED10) & UNASSIGNED_UMAP$UMAP_2 > -23 & UNASSIGNED_UMAP$UMAP_1 < -12) UNASSIGNED11 = subset(UNASSIGNED_Seu, idents = c(10,20)) UNASSIGNED11Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED11) & UNASSIGNED_UMAP$UMAP_1 > -11 & UNASSIGNED_UMAP$UMAP_1 < 1 & UNASSIGNED_UMAP$UMAP_2 > -20 & UNASSIGNED_UMAP$UMAP_2 < -5) UNASSIGNED12 = subset(UNASSIGNED_Seu, idents = c(11)) UNASSIGNED12Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED12) & UNASSIGNED_UMAP$UMAP_1 > 15 & UNASSIGNED_UMAP$UMAP_2 > 0) UNASSIGNED13 = subset(UNASSIGNED_Seu, idents = c(12, 18)) UNASSIGNED13Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED13) & UNASSIGNED_UMAP$UMAP_2 > 13) UNASSIGNED14 = subset(UNASSIGNED_Seu, idents = c(13)) UNASSIGNED14Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED14) & UNASSIGNED_UMAP$UMAP_2 < -20) UNASSIGNED15 = subset(UNASSIGNED_Seu, idents = c(14)) UNASSIGNED15Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED15) & UNASSIGNED_UMAP$UMAP_1 > 12 & UNASSIGNED_UMAP$UMAP_1 < 20 & UNASSIGNED_UMAP$UMAP_2 > -5 & UNASSIGNED_UMAP$UMAP_2 < 5) UNASSIGNED16 = subset(UNASSIGNED_Seu, idents = c(15)) UNASSIGNED16Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED16) & UNASSIGNED_UMAP$UMAP_1 > 16 & UNASSIGNED_UMAP$UMAP_2 > 10) UNASSIGNED17Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED16) & UNASSIGNED_UMAP$UMAP_2 < 10 & UNASSIGNED_UMAP$UMAP_1 < 20) UNASSIGNED18 = subset(UNASSIGNED_Seu, idents = c(16)) UNASSIGNED18Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED18) & UNASSIGNED_UMAP$UMAP_2 > 5) UNASSIGNED19 = subset(UNASSIGNED_Seu, idents = c(17)) UNASSIGNED20 = subset(UNASSIGNED_Seu, idents = c(19)) UNASSIGNED20Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED20) & UNASSIGNED_UMAP$UMAP_2 > -20) UNASSIGNED21 = subset(UNASSIGNED_Seu, idents = c(20)) UNASSIGNED21Clean = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED21) & UNASSIGNED_UMAP$UMAP_2 > 10 & UNASSIGNED_UMAP$UMAP_1 < 5) UNASSIGNED22 = subset(UNASSIGNED_Seu, idents = c(21)) #CheckInput = UNASSIGNED11Clean #CheckUMAP(UNASSIGNED_Seu) UNASSIGNED_REST = subset(UNASSIGNED_Seu, cells = c(row.names(UNASSIGNED01Clean), row.names(UNASSIGNED02Clean), row.names(UNASSIGNED03Clean), row.names(UNASSIGNED04Clean), row.names(UNASSIGNED05Clean), row.names(UNASSIGNED06Clean), row.names(UNASSIGNED07Clean), row.names(UNASSIGNED08Clean), row.names(UNASSIGNED09Clean), row.names(UNASSIGNED10Clean), row.names(UNASSIGNED11Clean), row.names(UNASSIGNED12Clean), row.names(UNASSIGNED13Clean), row.names(UNASSIGNED14Clean), row.names(UNASSIGNED15Clean), row.names(UNASSIGNED16Clean), row.names(UNASSIGNED17Clean), row.names(UNASSIGNED18Clean), colnames(UNASSIGNED19), row.names(UNASSIGNED20Clean), row.names(UNASSIGNED21Clean), colnames(UNASSIGNED22)), invert=T) #CheckInput = UNASSIGNED10_Extras #CheckUMAP(UNASSIGNED_Seu) UNASSIGNED02_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -15 & UNASSIGNED_UMAP$UMAP_2 > 0 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED02Clean)) UNASSIGNED03_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST)& UNASSIGNED_UMAP$UMAP_1 > -11 & UNASSIGNED_UMAP$UMAP_1 < 2 & UNASSIGNED_UMAP$UMAP_2 > -3 & UNASSIGNED_UMAP$UMAP_2 < 5 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED03Clean)) UNASSIGNED06_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 10 & UNASSIGNED_UMAP$UMAP_2 < -13 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED06Clean)) UNASSIGNED07_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > 10 & UNASSIGNED_UMAP$UMAP_1 > 5 & UNASSIGNED_UMAP$UMAP_1 < 16 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED07Clean)) UNASSIGNED08_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 20 & UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < 0.5 | UNASSIGNED_UMAP$UMAP_1 > 22 & UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < 1 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED08Clean)) UNASSIGNED10_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -23 & UNASSIGNED_UMAP$UMAP_2 < -14 & UNASSIGNED_UMAP$UMAP_1 < -12 | row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -15 & UNASSIGNED_UMAP$UMAP_2 < -10 & UNASSIGNED_UMAP$UMAP_1 < -12 & UNASSIGNED_UMAP$UMAP_1 > -20 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED10Clean)) UNASSIGNED11_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -13 & UNASSIGNED_UMAP$UMAP_1 < 0 & UNASSIGNED_UMAP$UMAP_2 > -20 & UNASSIGNED_UMAP$UMAP_2 < -8 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED10_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED11Clean)) UNASSIGNED12_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 18 & UNASSIGNED_UMAP$UMAP_2 > 0 & UNASSIGNED_UMAP$UMAP_2 < 12 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED08_Extras)| row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED12Clean)) UNASSIGNED14_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -8 & UNASSIGNED_UMAP$UMAP_2 < -20 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED10_Extras), row.names(UNASSIGNED11_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED14Clean)) UNASSIGNED15_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > 12 & UNASSIGNED_UMAP$UMAP_1 < 20 & UNASSIGNED_UMAP$UMAP_2 > -5 & UNASSIGNED_UMAP$UMAP_2 < 4 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED12_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED15Clean)) UNASSIGNED16_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > 12 & UNASSIGNED_UMAP$UMAP_1 > 12 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED07_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED16Clean)) UNASSIGNED17_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) &UNASSIGNED_UMAP$UMAP_2 > -10 & UNASSIGNED_UMAP$UMAP_2 < -6 & UNASSIGNED_UMAP$UMAP_1 > 13 & UNASSIGNED_UMAP$UMAP_1 < 20 | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED17Clean)) UNASSIGNED18_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -6 & UNASSIGNED_UMAP$UMAP_1 < 3 & UNASSIGNED_UMAP$UMAP_2 < 12 & UNASSIGNED_UMAP$UMAP_2 > 0 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED03_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED18Clean)) UNASSIGNED19_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -4 & UNASSIGNED_UMAP$UMAP_1 < 7 & UNASSIGNED_UMAP$UMAP_2 < -19 | row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED19)) UNASSIGNED20_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -17 & UNASSIGNED_UMAP$UMAP_2 > -16 & UNASSIGNED_UMAP$UMAP_2 < 5 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED10_Extras), row.names(UNASSIGNED02_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED20Clean)) UNASSIGNED04_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -9 & UNASSIGNED_UMAP$UMAP_2 < 4 & UNASSIGNED_UMAP$UMAP_1 > -18 & UNASSIGNED_UMAP$UMAP_1 < -3 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED03_Extras), row.names(UNASSIGNED11_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED04Clean)) UNASSIGNED09_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -6 & UNASSIGNED_UMAP$UMAP_2 < 14 & UNASSIGNED_UMAP$UMAP_2 > 3 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED02_Extras), row.names(UNASSIGNED03_Extras), row.names(UNASSIGNED04_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED09Clean)) UNASSIGNED13_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -15 & UNASSIGNED_UMAP$UMAP_1 < -1 & UNASSIGNED_UMAP$UMAP_2 > 12 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED09_Extras) | UNASSIGNED_UMAP$UMAP_1 > -1 & UNASSIGNED_UMAP$UMAP_1 < 5 & UNASSIGNED_UMAP$UMAP_2 > 18 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED09_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED13Clean)) UNASSIGNED21_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -5 & UNASSIGNED_UMAP$UMAP_1 < 5 & UNASSIGNED_UMAP$UMAP_2 > 12 & UNASSIGNED_UMAP$UMAP_2 < 20 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED13_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED21Clean)) UNASSIGNED05_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_2 > -1 & UNASSIGNED_UMAP$UMAP_2 < 11 & UNASSIGNED_UMAP$UMAP_1 > 3 & UNASSIGNED_UMAP$UMAP_1 < 15 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED15_Extras) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED05Clean)) UNASSIGNED01_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 > -3 & UNASSIGNED_UMAP$UMAP_1 < 17 & UNASSIGNED_UMAP$UMAP_2 > -23 & UNASSIGNED_UMAP$UMAP_2 < 4 & ! row.names(UNASSIGNED_UMAP) %in% c(row.names(UNASSIGNED19_Extras), row.names(UNASSIGNED17_Extras), row.names(UNASSIGNED15_Extras), row.names(UNASSIGNED11_Extras), row.names(UNASSIGNED05_Extras), row.names(UNASSIGNED06_Extras), row.names(UNASSIGNED03_Extras), row.names(UNASSIGNED18_Extras)) | row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED01Clean)) UNASSIGNED22_Extras = subset(UNASSIGNED_UMAP, row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED_REST) & UNASSIGNED_UMAP$UMAP_1 < -5 & UNASSIGNED_UMAP$UMAP_1 > -10 & UNASSIGNED_UMAP$UMAP_2 > 0 & UNASSIGNED_UMAP$UMAP_2 < -10 & ! row.names(UNASSIGNED_UMAP) %in% row.names(UNASSIGNED04_Extras) | row.names(UNASSIGNED_UMAP) %in% colnames(UNASSIGNED22)) #CheckInput = UNASSIGNED10_Extras #CheckUMAP(UNASSIGNED_Seu) UNASSIGNED_Assigns = GenerateMetaData_Barcodes(list("UNASSIGNED_01" = UNASSIGNED01_Extras, "UNASSIGNED_02" = UNASSIGNED02_Extras,"UNASSIGNED_03" = UNASSIGNED03_Extras, "UNASSIGNED_04" = UNASSIGNED04_Extras, "UNASSIGNED_05" = UNASSIGNED05_Extras, "UNASSIGNED_06" = UNASSIGNED06_Extras, "UNASSIGNED_07" = UNASSIGNED07_Extras, "UNASSIGNED_08" = UNASSIGNED08_Extras, "UNASSIGNED_09" = UNASSIGNED09_Extras, "UNASSIGNED_10" = UNASSIGNED10_Extras, "UNASSIGNED_11" = UNASSIGNED11_Extras, "UNASSIGNED_12" = UNASSIGNED12_Extras,"UNASSIGNED_13" = UNASSIGNED13_Extras, "UNASSIGNED_14" = UNASSIGNED14_Extras, "UNASSIGNED_15" = UNASSIGNED15_Extras, "UNASSIGNED_16" = UNASSIGNED16_Extras, "UNASSIGNED_17" = UNASSIGNED17_Extras, "UNASSIGNED_18" = UNASSIGNED18_Extras, "UNASSIGNED_19" = UNASSIGNED19_Extras,"UNASSIGNED_20" = UNASSIGNED20_Extras, "UNASSIGNED_21" = UNASSIGNED21_Extras, "UNASSIGNED_22" = UNASSIGNED22_Extras)) UNASSIGNED_01_Rest = subset(UNASSIGNED_Seu, cells = UNASSIGNED_Assigns$Barcodes, invert=T) UNASSIGNED_Assigns = GenerateMetaData(list("UNASSIGNED_01" = UNASSIGNED01_Extras, "UNASSIGNED_02" = UNASSIGNED02_Extras,"UNASSIGNED_03" = UNASSIGNED03_Extras, "UNASSIGNED_04" = UNASSIGNED04_Extras, "UNASSIGNED_05" = UNASSIGNED05_Extras, "UNASSIGNED_06" = UNASSIGNED06_Extras, "UNASSIGNED_07" = UNASSIGNED07_Extras, "UNASSIGNED_08" = UNASSIGNED08_Extras, "UNASSIGNED_09" = UNASSIGNED09_Extras, "UNASSIGNED_10" = UNASSIGNED10_Extras, "UNASSIGNED_11" = UNASSIGNED11_Extras, "UNASSIGNED_12" = UNASSIGNED12_Extras,"UNASSIGNED_13" = UNASSIGNED13_Extras, "UNASSIGNED_14" = UNASSIGNED14_Extras, "UNASSIGNED_15" = UNASSIGNED15_Extras, "UNASSIGNED_16" = UNASSIGNED16_Extras, "UNASSIGNED_17" = UNASSIGNED17_Extras, "UNASSIGNED_18" = UNASSIGNED18_Extras, "UNASSIGNED_19" = UNASSIGNED19_Extras,"UNASSIGNED_20" = UNASSIGNED20_Extras, "UNASSIGNED_21" = UNASSIGNED21_Extras, "UNASSIGNED_22" = UNASSIGNED22_Extras)) #, "UNASSIGNED_01" = UNASSIGNED_01_Rest)) UNASSIGNED_Assigns$Dups = duplicated(UNASSIGNED_Assigns$Barcodes) | duplicated(UNASSIGNED_Assigns$Barcodes, fromLast=T) UNASSIGNED_Assigns_T = subset(UNASSIGNED_Assigns, UNASSIGNED_Assigns$Dups == T) unique(UNASSIGNED_Assigns_T$Pop) #CheckInput = UNASSIGNED01_Extras #CheckUMAP(UNASSIGNED_Seu) save(list=c("GeneLists", "CheckUMAP", "ClusterFunc_All_RNA", "GenerateMetaData", "GenerateMetaData_Barcodes", "Microglia_Seu", "Microglia_Assigns", "VLMC_Seu", "VLMC_Assigns", "Pericytes_Seu", "Pericytes_Assigns", "SMC_Seu", "SMC_Assigns", "RG_Assigns", "RG_Seu","NE_Assigns", "NE_Seu", "IP_Assigns", "IP_Seu", "Div_Assigns", "Div_Seu", "Astro_Assigns", "Astro_Seu", "Endo_Assigns", "Endo_Seu", "Tany_Assigns", "Tany_Seu", "Ependy_Assigns", "Ependy_Seu", "Olig_Seu", "Olig_Assigns", "ARC_Seu", "ARC_Assigns", "PVH_Seu", "PVH_Assigns", "TM_Seu", "TM_Assigns", "VMH_Seu", "VMH_Assigns", "SMN_Seu", "SMN_Assigns", "LH_Seu", "LH_Assigns", "SCN_Seu", "SCN_Assigns", "DMH_Seu", "DMH_Assigns", "PO_Seu", "PO_Assigns", "AH_Seu", "AH_Assigns", "MN_Seu", "MN_Assigns", "UNASSIGNED_Seu", "UNASSIGNED_Assigns"), file = "~/Hypothalamus_Subclustering_APR2023.RData")
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Output subclustering
SeuList = list("ARC" = ARC_Seu, "PVH" = PVH_Seu, "VMH" = VMH_Seu,"SMN" = SMN_Seu, "LH" = LH_Seu, "TM" = TM_Seu, "SCN" = SCN_Seu, "DMH" = DMH_Seu, "PO" = PO_Seu, "AH" = AH_Seu, "MN" = MN_Seu, "UNASSIGNED" = UNASSIGNED_Seu, "Microglia" = Microglia_Seu, "VLMC" = VLMC_Seu, "Pericytes" = Pericytes_Seu, "SMC" = SMC_Seu, "Radial Glia" = RG_Seu, "Neurepithelial" = NE_Seu, "Dividing Progenitors" = Div_Seu, "Intermediate Progenitors" = IP_Seu, "Astrocytes" = Astro_Seu, "Oligodendrocytes" = Olig_Seu, "Ependymocytes" = Ependy_Seu, "Tanycytes" = Tany_Seu, "Endothelial" = Endo_Seu) AssignsList = list("ARC" = ARC_Assigns, "PVH" = PVH_Assigns, "VMH" = VMH_Assigns,"SMN" = SMN_Assigns, "LH" = LH_Assigns, "TM" = TM_Assigns, "SCN" = SCN_Assigns, "DMH" = DMH_Assigns, "PO" = PO_Assigns, "AH" = AH_Assigns, "MN" = MN_Assigns, "UNASSIGNED" = UNASSIGNED_Assigns, "Microglia" = Microglia_Assigns, "VLMC" = VLMC_Assigns, "Pericytes" = Pericytes_Assigns, "SMC" = SMC_Assigns, "Radial Glia" = RG_Assigns, "Neurepithelial" = NE_Assigns, "Dividing Progenitors" = Div_Assigns, "Intermediate Progenitors" = IP_Assigns, "Astrocytes" = Astro_Assigns, "Oligodendrocytes" = Olig_Assigns, "Ependymocytes" = Ependy_Assigns, "Tanycytes" = Tany_Assigns, "Endothelial" = Endo_Assigns) #Check all assigns are equal to number of cells for(x in names(AssignsList)){ cat(paste(x, dim(AssignsList[[x]])[1] - dim(SeuList[[x]])[2], "\n")) } #Count Clusters NCells = 0 AllAssigns = as.data.frame(matrix(ncol=1, nrow=0)) colnames(AllAssigns) = "Pop" for(x in names(AssignsList)){ NCells = NCells+length(unique(AssignsList[[x]]$Pop) ) AllAssigns = rbind(AllAssigns, AssignsList[[x]]) } #Generate a list of new assignments - includes Class and Subclass annotations - used in Glowworm Mapping Hypo_Assignments = as.data.frame(matrix(ncol=2, nrow=0)) colnames(Hypo_Assignments) = c("Class", "Subclass") for(x in names(AssignsList)){ PullAssigns = AssignsList[[x]] colnames(PullAssigns) = "Subclass" PullAssigns$Class = ifelse(x %in% c("ARC", "PVH", "VMH", "SMN", "LH", "TM", "SCN", "DMH", "PO", "AH", "MN", "UNASSIGNED"), "Neurons", x) #PullAssigns$Subclass = ifelse(x %in% "UNASSIGNED", "Unassigned", PullAssigns$Subclass) PullAssigns$Class = ifelse(PullAssigns$Subclass %in% grep(pattern = "^RG",x = PullAssigns$Subclass, value = T), "Radial Glia", PullAssigns$Class) Hypo_Assignments = rbind(Hypo_Assignments, PullAssigns) } Hypo_Assignments$Subclass = gsub("UNASSIGNED", "Ukn", Hypo_Assignments$Subclass) write.csv(Hypo_Assignments, "~/Library/CloudStorage/Box-Box/HG2553 Main Folder/Hypo_Assignments_1MAY23.csv") #Output all files - single page on one pds OutPlot = list() for(x in names(SeuList)){ GetSeu = SeuList[[x]] GetMeta = AssignsList[[x]] GetSeu = AddMetaData(GetSeu, GetMeta, "Assigns") Idents(GetSeu) = "Assigns" OutPlot[[x]] = DimPlot(GetSeu, label=T)+NoLegend()+ggtitle(x) #Idents(GetSeu) = "SampleAdult" #OutPlot[[paste(x, "samples")]] = DimPlot(GetSeu, label=T)+NoLegend()+ggtitle(x) } pdf("CluseteredPlots_Hypothalamus_1MAY23.pdf", width = 8, height = 8) print(OutPlot) dev.off() #CluseteredPlots_Hypothalamus_5APR23.pdf #Output plots - all on one page OutPlot = list() for(x in c("Radial Glia", "Neurepithelial" ,"Dividing Progenitors", "Intermediate Progenitors", "Astrocytes", "Oligodendrocytes", "Microglia", "VLMC", "Pericytes" ,"SMC", "TM", "ARC", "PVH", "VMH", "LH", "DMH", "SCN", "PO", "AH", "SMN", "MN", "UNASSIGNED")){ Title = ifelse(x %in% c("ARC", "PVH", "VMH", "SMN", "LH", "TM", "SCN", "DMH", "PO", "AH", "MN"), paste("Neurons:", x), x) Title = ifelse(x %in% "UNASSIGNED", "Neurons: Unassigned", Title) GetSeu = SeuList[[x]] GetMeta = AssignsList[[x]] GetSeu = AddMetaData(GetSeu, GetMeta, "Assigns") Idents(GetSeu) = "Assigns" OutPlot[[x]] = DimPlot(GetSeu, label=F)+NoLegend()+ggtitle(Title) + theme(axis.line = element_line(color = "lightgrey"), axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank(), plot.title = element_text(size = 10)) } Patch = wrap_plots(OutPlot) + plot_layout(design = "##ABCD ##EFGH IJKLMN OPQRST UV####") pdf("Fetal_Figure_28APR22.pdf", width = 12, height = 12) print(Patch) dev.off() #3D UMAP - main annotations CellPopColors = c("Neurepithelial"= "#aad576", "Dividing Progenitors"= "#78a02d", "Radial Glia" = "#538d22", "Intermediate Progenitors" = "#245501", "Oligodendrocytes"= "#59a5d8", "Neurons"= "#7251b5", "Astrocytes" = "#ff7aa2", "Ependymocytes" = "#ff9ebb", "Tanycytes" = "#ffc2d4", "Microglia" = "#d64050", "Pericytes" = "#fecf29" , "SMC" ="#fee08b", "VLMC" = "#faad60", "Endothelial" = "#f36c44", "Blood" ="#9e1b44") #19 CleanedClusters_Figure1 = read.csv("~/Dropbox/Columbia/Brian Hypothalamus/ScienceAdvances_2023/Figure 1/CleanedClusters_Figure1_19DEC22.csv", row.names =1) CleanedClusters_Figure1 = merge(CleanedClusters_Figure1, Hypo_Assignments, by.x = "Row.names", by.y = 0) CleanedClusters_Figure1 = subset(CleanedClusters_Figure1, CleanedClusters_Figure1$Row.names %in% colnames(AllHypo)) p = plot_ly(CleanedClusters_Figure1, x = ~UMAP_3, y = ~UMAP_2, z = ~UMAP_1, size = 1, color = ~Class, colors = CellPopColors, type="scatter3d") htmlwidgets::saveWidget(p, "HYPOTHALAMUS_AllAssignments_GLOWWORM.html")
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