vignettes/vignettes.md
In chenweng1991/scMitoTracing: R package for Regulatory multi-omics with Deep Mitochondrial mutation profiling
Quick start
Variant parsing and analysis
library(scMitoTracing)
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor01_CD34_1_Multiomekit/MTenrichCombine/Enrich/CW_mgatk/final"
DN1CD34_1.depth<-DepthSummary(WD)
DN1CD34_1.VariantsGTSummary<-CW_mgatk.read(WD,Processed =T)
DN1CD34_1.Variants.feature.lst<-Vfilter_v3(InputSummary=DN1CD34_1.VariantsGTSummary,depth=DN1CD34_1.depth)
Plotting for variants
Plot for depth
plot_depth(DN1CD34_1.depth$Total,"Total")
plot_depth(DN1CD34_1.depth$VerySensitive,"VerySensitive")
Plot for variant signature
options(repr.plot.width=16, repr.plot.height=6)
ps<-list()
for(name in names(DN1CD34_1.Variants.feature.lst)){
p<-MutationProfile.bulk(DN1CD34_1.Variants.feature.lst[[name]]$Variants)+ggtitle(name)+theme(title =element_text(size=20))
ps<-c(ps,list(p))
}
grid.arrange(grobs=ps)
Plot rssential variant metrics
plot_variant(DN1CD34_1.VariantsGTSummary,DN1CD34_1.Variants.feature.lst,depth=DN1CD34_1.depth,cat=c("Total","VerySensitive","Sensitive","Specific"),p4xlim = 30)
Advanced options for variant parsing
DepthSummary from a subset of cells
library(scMitoTracing)
library(EZsinglecell2)
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor01_CD34_1_Multiomekit/MTenrichCombine/Enrich/CW_mgatk/final"
## Read in the CD34 Seurat object
Donor01_CD34_1_Multiome_wrapper<-readRDS("/lab/solexa_weissman/cweng/Projects/RDS/Donor01_CD34_1_Multiome_wrapper.RDS")
## Translate the RNA names into ATAC/Mito names
Donor01_CD34_1_Multiome.meta<-Translate_RNA2ATAC(Donor01_CD34_1_Multiome_wrapper$seurat@meta.data)
## Select HSC cells and parse the HSC mito depth information
HSCcells<-subset(Donor01_CD34_1_Multiome.meta,CellType=="HSC")$ATACName
DN1CD34_1.depth<-DepthSummary(WD,Processed = F,CellSubset = HSCcells,cellSubSetName = "HSC")
Making tree
Below is an example that combine both CD34 and BMMC data into one mitoTracing object
library(EZsinglecell2)
## CD34 This is the same as above
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor01_CD34_1_Multiomekit/MTenrichCombine/Enrich/CW_mgatk/final"
#DN1CD34_1.depth<-DepthSummary(WD)
DN1CD34_1.depth<-readRDS(paste(WD,"/DN1CD34_1.depth.RDS",sep=""))
DN1CD34_1.VariantsGTSummary<-CW_mgatk.read(WD,Processed =T)
DN1CD34_1.Variants.feature.lst<-Vfilter_v3(InputSummary=DN1CD34_1.VariantsGTSummary,depth=DN1CD34_1.depth)
## BMMC1
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/Run210706L2/COMBINE_CALLV/CW_mgatk_Full/final"
# DN1BMMC1.depth<-DepthSummary(WD)
#saveRDS(DN1BMMC1.depth,paste(WD,"/DN1BMMC1.depth.RDS",sep=""))
DN1BMMC1.depth<-readRDS(paste(WD,"/DN1BMMC1.depth.RDS",sep=""))
DN1BMMC1.VariantsGTSummary<-CW_mgatk.read(WD,Processed =T)
DN1BMMC1.Variants.feature.lst<-Vfilter_v3(InputSummary=DN1BMMC1.VariantsGTSummary,depth=DN1BMMC1.depth)
## Make meta
bmmc.filtered<-readRDS("/lab/solexa_weissman/cweng/Projects/RDS/mutiome.donor01_bmmc01.RNA.filtered.RDS")
Donor01_CD34_1_Multiome_wrapper<-readRDS("/lab/solexa_weissman/cweng/Projects/RDS/Donor01_CD34_1_Multiome_wrapper.RDS")
DN1BMMC1.meta<-Translate_RNA2ATAC(meta=bmmc.filtered@meta.data[,c("nCount_RNA","nFeature_RNA","CellType")])
DN1CD34_1.meta<-Translate_RNA2ATAC(meta=Donor01_CD34_1_Multiome_wrapper$seurat@meta.data[,c("nCount_RNA","nFeature_RNA","CellType")])
## Make CD34_BMMC_mitoTracing.VerySensitive
CD34_BMMC_mitoTracing.VerySensitive<-Create_mitoTracing(GTsummary_list=list(DN1CD34_1.VariantsGTSummary,DN1BMMC1.VariantsGTSummary),
depth_list=list(DN1CD34_1.depth,DN1BMMC1.depth),
feature.list_list=list(DN1CD34_1.Variants.feature.lst,DN1BMMC1.Variants.feature.lst),
meta_list=list(DN1CD34_1.meta,DN1BMMC1.meta),
thr="VerySensitive",
labels=c("CD34","BMMC"))
## Show basics of the CD34_BMMC_mitoTracing.VerySensitive
CD34_BMMC_mitoTracing.VerySensitive
Make variant matrix and clonal clustering
CD34_BMMC_mitoTracing.VerySensitive<-Make_matrix(CD34_BMMC_mitoTracing.VerySensitive)
CD34_BMMC_mitoTracing.VerySensitive<-SeuratLSIClustering(CD34_BMMC_mitoTracing.VerySensitive)
CD34_BMMC_mitoTracing.VerySensitive<-AddDatatoplot_clustering(CD34_BMMC_mitoTracing.VerySensitive)
Clonal clustering and distance metrics (Updated 3/30/22)
## Make a weight matrix (For different variants) based on recurrencey. It is required for weighted distance calculation
data(CellPCT)
V.weight<-data.frame(weight=1-CellPCT$muRate)
V.weight$Variants<-paste("Variants",gsub("_","",CellPCT$Variant),sep="")
BMMC_mitoTracing<-SeuratLSIClustering(BMMC_mitoTracing,lsidim=2:50,rmvariants=c("Variants310TC","Variants3109TC","Variants5764CT"))
BMMC_mitoTracing<-AddDist(BMMC_mitoTracing,weightDF=V.weight,LSIdist=T,dim=2:50)
Subset a mitotracing object (Updated 5/8/22)
# Example from HSC_multiome_Het.ipynb
DN4_stemcell_mitoTracing.seed.verysensitive<-Subset_MitoTracing(DN4_stemcell_mitoTracing.verysensitive,Cells=topcell)
DN4_stemcell_mitoTracing.seed.verysensitive<SeuratLSIClustering(DN4_stemcell_mitoTracing.seed.verysensitive,res=2,lsidim=3:50,rmvariants=c("Variants310TC","Variants3109TC","Variants5764CT"))
DN4_stemcell_mitoTracing.seed.verysensitive<-AddDist(DN4_stemcell_mitoTracing.seed.verysensitive,weightDF=V.weight,LSIdist=T,dim=3:50)
Make tree (Updated 5/8/22)
DN4_stemcell_mitoTracing.seed.verysensitive<-Make_tree(DN4_stemcell_mitoTracing.seed.verysensitive,d = "w_jaccard",algorithm = "nj",onlyreturntree = F)
Add depth, assign variant to tree (Updated 5/8/22)
## Load depth
## Prepare the depth, given the order created: "BMMC":-1 ,"HSPC": -2, "HSC": -3
#DN4_WD_BMMC="/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor4Donor9/Donor4/DN4_BMMC/MTenrichCombine/mitoV/final/"
DN4_WD_HSPC="/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor4Donor9/Donor4/DN4_HPC/MTenrichCombine/mitoV/final/"
DN4_WD_HSC="/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor4Donor9/Donor4/DN4_HSC/MTenrichCombine/mitoV/final/"
q_HSPC<-read.table(paste(DN4_WD_HSPC,"QualifiedTotalCts",sep=""))
q_HSC<-read.table(paste(DN4_WD_HSC,"QualifiedTotalCts",sep=""))
q_HSPC$V1<-paste(q_HSPC$V1,"2",sep="_")
q_HSC$V1<-paste(q_HSC$V1,"3",sep="_")
quality<-rbind(q_HSPC,q_HSC)
DN4_stemcell.seed.depth<-subset(quality,V1 %in% DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Cell)
## add depth
DN4_stemcell_mitoTracing.seed.verysensitive<-Add_DepthMatrix(DN4_stemcell_mitoTracing.seed.verysensitive,quality)
## maximum-liklihood-based variant assignment
DN4_stemcell_mitoTracing.seed.verysensitive<-Add_AssignVariant(DN4_stemcell_mitoTracing.seed.verysensitive,n.cores = 8)
Make Allnodes(Node|Parent|Freq|CladeSize), visulize the branchlength by number of assigned variants (Updated 5/8/22)
Allnodes=MakeAllNodes(mitoTracing,prob.cut=0.1)
TreeData<-DN4_stemcell_mitoTracing.seed.verysensitive@TREE@phylo
TreeData$edge.length<-Allnodes[match(DN4_stemcell_mitoTracing.seed.verysensitive@TREE@phylo$edge[,2],Allnodes$Node),] %>% .$Freq
options(repr.plot.width=3, repr.plot.height=24,repr.plot.res=120)
as.treedata(TreeData) %>% ggtree(.)+theme_tree2()
Cut the tree in to clones(Updated 5/8/22)
A useful tool from phanforn[https://rdrr.io/cran/phangorn/man/Ancestors.html]
DN4_stemcell_mitoTracing.seed.verysensitive<-Add_tree_cut(DN4_stemcell_mitoTracing.seed.verysensitive,MinCell = 30,N = 1, prob.cut = 0.3)
#Visulize
DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Clone_merge<-as.factor(DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Clone_merge)
library(RColorBrewer)
n=length(unique(DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Clone_merge))
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
options(repr.plot.width=6, repr.plot.height=6,repr.plot.res=100)
p<-ggtree(DN4_stemcell_mitoTracing.seed.verysensitive@TREE@treedata,layout="circular", branch.length='none')
p+geom_fruit(
data=DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta,
geom=geom_tile,
mapping=aes(y=Cell,x=2,fill=Clone_merge),
pwidth=0.001,
width=3,
offset=0.05
)+scale_fill_manual(values=col_vector)
chenweng1991/scMitoTracing documentation built on July 2, 2024, 8:41 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Quick start
Variant parsing and analysis
library(scMitoTracing)
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor01_CD34_1_Multiomekit/MTenrichCombine/Enrich/CW_mgatk/final"
DN1CD34_1.depth<-DepthSummary(WD)
DN1CD34_1.VariantsGTSummary<-CW_mgatk.read(WD,Processed =T)
DN1CD34_1.Variants.feature.lst<-Vfilter_v3(InputSummary=DN1CD34_1.VariantsGTSummary,depth=DN1CD34_1.depth)
Plotting for variants
Plot for depth
plot_depth(DN1CD34_1.depth$Total,"Total")
plot_depth(DN1CD34_1.depth$VerySensitive,"VerySensitive")
Plot for variant signature
options(repr.plot.width=16, repr.plot.height=6)
ps<-list()
for(name in names(DN1CD34_1.Variants.feature.lst)){
p<-MutationProfile.bulk(DN1CD34_1.Variants.feature.lst[[name]]$Variants)+ggtitle(name)+theme(title =element_text(size=20))
ps<-c(ps,list(p))
}
grid.arrange(grobs=ps)
Plot rssential variant metrics
plot_variant(DN1CD34_1.VariantsGTSummary,DN1CD34_1.Variants.feature.lst,depth=DN1CD34_1.depth,cat=c("Total","VerySensitive","Sensitive","Specific"),p4xlim = 30)
Advanced options for variant parsing
DepthSummary from a subset of cells
library(scMitoTracing)
library(EZsinglecell2)
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor01_CD34_1_Multiomekit/MTenrichCombine/Enrich/CW_mgatk/final"
## Read in the CD34 Seurat object
Donor01_CD34_1_Multiome_wrapper<-readRDS("/lab/solexa_weissman/cweng/Projects/RDS/Donor01_CD34_1_Multiome_wrapper.RDS")
## Translate the RNA names into ATAC/Mito names
Donor01_CD34_1_Multiome.meta<-Translate_RNA2ATAC(Donor01_CD34_1_Multiome_wrapper$seurat@meta.data)
## Select HSC cells and parse the HSC mito depth information
HSCcells<-subset(Donor01_CD34_1_Multiome.meta,CellType=="HSC")$ATACName
DN1CD34_1.depth<-DepthSummary(WD,Processed = F,CellSubset = HSCcells,cellSubSetName = "HSC")
Making tree
Below is an example that combine both CD34 and BMMC data into one mitoTracing object
library(EZsinglecell2)
## CD34 This is the same as above
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor01_CD34_1_Multiomekit/MTenrichCombine/Enrich/CW_mgatk/final"
#DN1CD34_1.depth<-DepthSummary(WD)
DN1CD34_1.depth<-readRDS(paste(WD,"/DN1CD34_1.depth.RDS",sep=""))
DN1CD34_1.VariantsGTSummary<-CW_mgatk.read(WD,Processed =T)
DN1CD34_1.Variants.feature.lst<-Vfilter_v3(InputSummary=DN1CD34_1.VariantsGTSummary,depth=DN1CD34_1.depth)
## BMMC1
WD<-"/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/Run210706L2/COMBINE_CALLV/CW_mgatk_Full/final"
# DN1BMMC1.depth<-DepthSummary(WD)
#saveRDS(DN1BMMC1.depth,paste(WD,"/DN1BMMC1.depth.RDS",sep=""))
DN1BMMC1.depth<-readRDS(paste(WD,"/DN1BMMC1.depth.RDS",sep=""))
DN1BMMC1.VariantsGTSummary<-CW_mgatk.read(WD,Processed =T)
DN1BMMC1.Variants.feature.lst<-Vfilter_v3(InputSummary=DN1BMMC1.VariantsGTSummary,depth=DN1BMMC1.depth)
## Make meta
bmmc.filtered<-readRDS("/lab/solexa_weissman/cweng/Projects/RDS/mutiome.donor01_bmmc01.RNA.filtered.RDS")
Donor01_CD34_1_Multiome_wrapper<-readRDS("/lab/solexa_weissman/cweng/Projects/RDS/Donor01_CD34_1_Multiome_wrapper.RDS")
DN1BMMC1.meta<-Translate_RNA2ATAC(meta=bmmc.filtered@meta.data[,c("nCount_RNA","nFeature_RNA","CellType")])
DN1CD34_1.meta<-Translate_RNA2ATAC(meta=Donor01_CD34_1_Multiome_wrapper$seurat@meta.data[,c("nCount_RNA","nFeature_RNA","CellType")])
## Make CD34_BMMC_mitoTracing.VerySensitive
CD34_BMMC_mitoTracing.VerySensitive<-Create_mitoTracing(GTsummary_list=list(DN1CD34_1.VariantsGTSummary,DN1BMMC1.VariantsGTSummary),
depth_list=list(DN1CD34_1.depth,DN1BMMC1.depth),
feature.list_list=list(DN1CD34_1.Variants.feature.lst,DN1BMMC1.Variants.feature.lst),
meta_list=list(DN1CD34_1.meta,DN1BMMC1.meta),
thr="VerySensitive",
labels=c("CD34","BMMC"))
## Show basics of the CD34_BMMC_mitoTracing.VerySensitive
CD34_BMMC_mitoTracing.VerySensitive
Make variant matrix and clonal clustering
CD34_BMMC_mitoTracing.VerySensitive<-Make_matrix(CD34_BMMC_mitoTracing.VerySensitive)
CD34_BMMC_mitoTracing.VerySensitive<-SeuratLSIClustering(CD34_BMMC_mitoTracing.VerySensitive)
CD34_BMMC_mitoTracing.VerySensitive<-AddDatatoplot_clustering(CD34_BMMC_mitoTracing.VerySensitive)
Clonal clustering and distance metrics (Updated 3/30/22)
## Make a weight matrix (For different variants) based on recurrencey. It is required for weighted distance calculation
data(CellPCT)
V.weight<-data.frame(weight=1-CellPCT$muRate)
V.weight$Variants<-paste("Variants",gsub("_","",CellPCT$Variant),sep="")
BMMC_mitoTracing<-SeuratLSIClustering(BMMC_mitoTracing,lsidim=2:50,rmvariants=c("Variants310TC","Variants3109TC","Variants5764CT"))
BMMC_mitoTracing<-AddDist(BMMC_mitoTracing,weightDF=V.weight,LSIdist=T,dim=2:50)
Subset a mitotracing object (Updated 5/8/22)
# Example from HSC_multiome_Het.ipynb
DN4_stemcell_mitoTracing.seed.verysensitive<-Subset_MitoTracing(DN4_stemcell_mitoTracing.verysensitive,Cells=topcell)
DN4_stemcell_mitoTracing.seed.verysensitive<SeuratLSIClustering(DN4_stemcell_mitoTracing.seed.verysensitive,res=2,lsidim=3:50,rmvariants=c("Variants310TC","Variants3109TC","Variants5764CT"))
DN4_stemcell_mitoTracing.seed.verysensitive<-AddDist(DN4_stemcell_mitoTracing.seed.verysensitive,weightDF=V.weight,LSIdist=T,dim=3:50)
Make tree (Updated 5/8/22)
DN4_stemcell_mitoTracing.seed.verysensitive<-Make_tree(DN4_stemcell_mitoTracing.seed.verysensitive,d = "w_jaccard",algorithm = "nj",onlyreturntree = F)
Add depth, assign variant to tree (Updated 5/8/22)
## Load depth
## Prepare the depth, given the order created: "BMMC":-1 ,"HSPC": -2, "HSC": -3
#DN4_WD_BMMC="/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor4Donor9/Donor4/DN4_BMMC/MTenrichCombine/mitoV/final/"
DN4_WD_HSPC="/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor4Donor9/Donor4/DN4_HPC/MTenrichCombine/mitoV/final/"
DN4_WD_HSC="/lab/solexa_weissman/cweng/Projects/MitoTracing_Velocity/SecondaryAnalysis/Donor4Donor9/Donor4/DN4_HSC/MTenrichCombine/mitoV/final/"
q_HSPC<-read.table(paste(DN4_WD_HSPC,"QualifiedTotalCts",sep=""))
q_HSC<-read.table(paste(DN4_WD_HSC,"QualifiedTotalCts",sep=""))
q_HSPC$V1<-paste(q_HSPC$V1,"2",sep="_")
q_HSC$V1<-paste(q_HSC$V1,"3",sep="_")
quality<-rbind(q_HSPC,q_HSC)
DN4_stemcell.seed.depth<-subset(quality,V1 %in% DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Cell)
## add depth
DN4_stemcell_mitoTracing.seed.verysensitive<-Add_DepthMatrix(DN4_stemcell_mitoTracing.seed.verysensitive,quality)
## maximum-liklihood-based variant assignment
DN4_stemcell_mitoTracing.seed.verysensitive<-Add_AssignVariant(DN4_stemcell_mitoTracing.seed.verysensitive,n.cores = 8)
Make Allnodes(Node|Parent|Freq|CladeSize), visulize the branchlength by number of assigned variants (Updated 5/8/22)
Allnodes=MakeAllNodes(mitoTracing,prob.cut=0.1)
TreeData<-DN4_stemcell_mitoTracing.seed.verysensitive@TREE@phylo
TreeData$edge.length<-Allnodes[match(DN4_stemcell_mitoTracing.seed.verysensitive@TREE@phylo$edge[,2],Allnodes$Node),] %>% .$Freq
options(repr.plot.width=3, repr.plot.height=24,repr.plot.res=120)
as.treedata(TreeData) %>% ggtree(.)+theme_tree2()
Cut the tree in to clones(Updated 5/8/22)
A useful tool from phanforn[https://rdrr.io/cran/phangorn/man/Ancestors.html]
DN4_stemcell_mitoTracing.seed.verysensitive<-Add_tree_cut(DN4_stemcell_mitoTracing.seed.verysensitive,MinCell = 30,N = 1, prob.cut = 0.3)
#Visulize
DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Clone_merge<-as.factor(DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Clone_merge)
library(RColorBrewer)
n=length(unique(DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta$Clone_merge))
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
options(repr.plot.width=6, repr.plot.height=6,repr.plot.res=100)
p<-ggtree(DN4_stemcell_mitoTracing.seed.verysensitive@TREE@treedata,layout="circular", branch.length='none')
p+geom_fruit(
data=DN4_stemcell_mitoTracing.seed.verysensitive@CellMeta,
geom=geom_tile,
mapping=aes(y=Cell,x=2,fill=Clone_merge),
pwidth=0.001,
width=3,
offset=0.05
)+scale_fill_manual(values=col_vector)
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