In Hannahglover/Glowworm: Mapping genes to single cell RNAseq

library(Seurat)
library(tidyverse)
library(ggpubr)
library(matrixStats)
library(unikn)
library(patchwork)
library(vautils)
library(scales)
library(MASS)
library(plyr)
library(reshape2)
library(beepr)
library(ggrepel)
####
FilePath = "/Users/hannahglover/Library/CloudStorage/Box-Box/HG2553 Main Folder/_Collins Lab Collab/Glowworm v4 - Background/"
hypo.combined = readRDS("/Users/hannahglover/Library/CloudStorage/Box-Box/HG2553 Main Folder/Science Advances/Hypo.combined_1JUNE23.rds")
Hypo_Assignments = read.csv("/Users/hannahglover/Library/CloudStorage/Box-Box/HG2553 Main Folder/_Collins Lab Collab/Hypo_Assignments_1MAY23.csv", row.names =1)

load("/Users/hannahglover/Library/CloudStorage/Box-Box/HG2553 Main Folder/_Collins Lab Collab/R Notebooks and RDS/Islets_Glowworm_28MAR23.RData") #"Islet_Assigns_MAR23", "Islet.combined"

load("/Users/hannahglover/Library/CloudStorage/Box-Box/HG2553 Main Folder/_Collins Lab Collab/R Notebooks and RDS/TabulaSapiens_Seurat_CLEANED.RData")
TS_Meta = TS_Clean@meta.data %>% dplyr::select("organ_tissue",  "CleanedAnnotation", "nFeature_RNA", "nCount_RNA")
TS_Meta$organ_tissue =  gsub("_", "", gsub("Lymph_Node", "Lymph", TS_Meta$organ_tissue))
TS_Meta$CleanedAnnotation =  gsub("Alpha Beta", "A/B", gsub("Alpha-Beta", "A/B", gsub("- Mesenchymal Cell -", "",  TS_Meta$CleanedAnnotation)))
TS_Meta$Comb = paste(TS_Meta$organ_tissue, TS_Meta$CleanedAnnotation, sep = " - ")

Colors

Colors24 = c("#732e68","#882255", "#5f183c", "#661100", "#991900", "#ac394c", "#d24e37", "#e67300","#e0a43a", "#DDCC77", "#999933", "#606020","#073416", "#117733", "#2e7368", "#44AA99", "#6699CC","#3973ac","#45afe4","#88CCEE", "#1c134b",  "#332288", "#a2a2a2","#626262")


Colors14 = c("#882255",  "#661100",  "#ac394c", "#d24e37", "#e67300", "#DDCC77", "#999933", "#073416", "#117733",  "#44AA99", "#6699CC","#3973ac", "#1c134b",  "#332288")

Colors11 = c("#882255",  "#661100",  "#ac394c","#e67300", "#DDCC77",  "#117733",  "#44AA99", "#6699CC","#3973ac", "#1c134b",  "#332288")

Hypothalamus

Hypo_AssignmentsClass = Hypo_Assignments %>% dplyr::select(Class)
hypo.combined = AddMetaData(hypo.combined, Hypo_AssignmentsClass, "Class")
Idents(hypo.combined) = "Class"

DefaultAssay(hypo.combined) = "integrated"
hypo.combined = ScaleData(hypo.combined, vars.to.regress = "percent.mt")
hypo.combined = FindVariableFeatures(hypo.combined)
hypo.combined = RunPCA(hypo.combined, dims = 20)
hypo.combined = RunUMAP(hypo.combined, dims = 1:20, n.components = 3)

hypo_3D = as.data.frame(hypo.combined@reductions$umap@cell.embeddings)
hypo_3D = merge(hypo_3D, Hypo_Assignments, by = 0)
hypo_3D$Class = factor(hypo_3D$Class, levels = c("Neurepithelial", "Dividing Progenitors", "Radial Glia", "Intermediate Progenitors", "Neurons", "Oligodendrocytes", "Astrocytes","Ependymocytes", "Tanycytes", "Microglia", "Endothelial","Pericytes", "SMC", "VLMC"))

p = plot_ly(hypo_3D, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, size = 1, sizes = 1, color = ~Class, colors = Colors14)
htmlwidgets::saveWidget(p, paste(FilePath, "HypoUMAP.html", sep=""))


pdf(paste0(FilePath, "HypoUMAP.pdf"), width = 10, height = 10)
print(DimPlot(hypo.combined, pt.size = 1)) + theme_classic() + theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank()) + scale_color_manual(values= Colors14)
dev.off()

Islets

Islets_AssignmentsClass = Islet_Assigns_MAR23 %>% dplyr::select(Broad)
Islets_AssignmentsClass$Broad = factor(Islets_AssignmentsClass$Broad, levels = c("Alpha", "Beta", "Delta", "Epsilon", "Gamma", "Acinar", "Ductal", "Macro", "Stellate", "Endothelial"))
Islet.combined = AddMetaData(Islet.combined, Islets_AssignmentsClass, "Class")
Idents(Islet.combined) = "Class"

pdf(paste0(FilePath, "IsletsUMAP.pdf"), width = 10, height = 10)
print(DimPlot(Islet.combined, pt.size = 1, reduction = "umap")) + theme_classic() + theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank())+ scale_color_manual(values= Colors11)+NoLegend()
dev.off()

DefaultAssay(Islet.combined) = "integrated"
Islet.combined = RunUMAP(Islet.combined, dims = 1:20, n.components = 3, spread=2)

Islets_3D = as.data.frame(Islet.combined@reductions$umap@cell.embeddings)
Islets_3D = merge(Islets_3D, Islet_Assigns_MAR23, by = 0)
Islets_3D$Broad = factor(Islets_3D$Broad, levels = c("Alpha", "Beta", "Delta", "Epsilon", "Gamma", "Acinar", "Ductal", "Macro", "Stellate", "Endothelial"))

p = plot_ly(Islets_3D, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, size = 3, sizes = 3, color = ~Broad, colors = Colors11)
htmlwidgets::saveWidget(p, paste(FilePath, "IsletsUMAP.html", sep=""))

Tabula Sapiens

Idents(TS_Clean) = "organ_tissue"

pdf(paste0(FilePath, "TabulaUMAP.pdf"), width = 10, height = 10)
print(DimPlot(TS_Clean, pt.size = 1, reduction = "umap", raster= T)) + theme_classic() + theme(axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank())+ scale_color_manual(values= Colors24)+NoLegend()
dev.off()

TS_Clean = RunUMAP(TS_Clean, dims = 1:30, n.components = 3, spread=2)

TS_3D = as.data.frame(TS_Clean@reductions$umap@cell.embeddings)
TS_3D = merge(TS_3D, TS_Meta, by = 0)
p = plot_ly(TS_3D, x = ~UMAP_1, y = ~UMAP_2, z = ~UMAP_3, size = 1, sizes = 1, color = ~organ_tissue, colors = Colors24)
htmlwidgets::saveWidget(p, paste(FilePath, "TS_UMAP.html", sep=""))

SAVE

save(list=c("TS_3D", "TS_Clean", "TS_Meta", "Islets_3D", "Islet.combined", "Islet_Assigns_MAR23", "hypo.combined", "Hypo_Assignments", "hypo_3D"), file = paste0(FilePath, "GW_DatasetPlots_InputData.RData"))