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Figure2_Figure6ab_SupFigure2bcde.Rmd
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Figure2_Figure6ab_SupFigure2bcde.Rmd
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---
title: "Figure 2, Figure 6ab, Sup Figure 2bcde"
author: "Mehdi Karimi"
date: "30/04/2020"
output:
html_document:
code_folding: hide
highlight: zenburn
number_sections: yes
theme: yeti
toc: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dyno)
library(tidyverse)
library(velocyto.R)
library(DESeq2)
library("Matrix")
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(ggpubr))
library(gam)
library(splatter)
library(slingshot)
library(DT)
library(Seurat)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(scater)
library(scran)
library(knitr)
library(org.Mm.eg.db)
library(cluster)
library(dynamicTreeCut)
library(SCORPIUS)
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(ggpubr))
suppressPackageStartupMessages(library(magrittr))
suppressPackageStartupMessages(library(metaMA))
suppressPackageStartupMessages(library(DT))
library(VennDiagram)
library("jpeg")
library("tiff")
library(ggplot2)
library(cowplot)
library(gridExtra)
library(knitr)
library(kableExtra)
```
```{r,echo=T, eval=T, cache=T, message=F, warning=FALSE, fig.width=5, fig.height=5}
####################
# Loading rep2 data
####################
ldat_new <- read.loom.matrices("/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/scRNAseq_CRG_02012020/onefilepercell_HFWL7BBXY_1_IDT-DUI-NXT-66_and_others_0VWWZ.loom")
ldat_new <- lapply(ldat_new,function(x) {
colnames(x) <- gsub(".bam","",gsub("onefilepercell_HFWL7BBXY_1_IDT-DUI-NXT-66_and_others_0VWWZ:","",colnames(x)))
x
})
meta_data <- read.csv(file="/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/scRNAseq_CRG_02012020/sample_info.csv", header=TRUE, sep=",")
rownames(meta_data) <- meta_data$sample_ID
meta_data$rep <- 'rep2'
meta_data <- meta_data[,c('rep','sample_label')]
colnames(meta_data) <- c('rep','celltype')
meta_data$celltype <- gsub('TCRint_DP_WT','TCRhiDP',gsub('CD8_SP_TCRpos_WT','CD8SP',gsub("CD4_SP_WT","CD4SP",gsub("CD4_pos_8lo_WT","CD4+8low",gsub("CD69_pos_DP_WT","CD69posDP",gsub("CD69_neg_DP_WT","CD69negDP",meta_data$celltype))))))
meta_data$celltype <- gsub('TCRint_DP_MHCclassII_KO','TCRhiDP_C2KO',gsub('CD8_SP_TCRpos_MHCclassII_KO','CD8SP_C2KO',gsub("CD4_SP_MHCclassII_KO","CD4SP_C2KO",gsub("CD4_pos_8lo_classII_KO","CD4+8low_C2KO",gsub("CD69_pos_DP_MHCclassII_KO","CD69posDP_C2KO",gsub("CD69_neg_DP_MHCclassII_KO","CD69negDP_C2KO",meta_data$celltype))))))
meta_data <- meta_data[!(rownames(meta_data)=="HFWL7BBXY_1_IDT-DUI-NXT-49" | rownames(meta_data)=="HFWL7BBXY_1_IDT-DUI-NXT-333"),]
meta_data[grep('C2KO',meta_data$celltype),]$rep <- 'rep2_KO'
meta_data$celltype <- gsub('_C2KO','', meta_data$celltype)
# exonic read (spliced) expression matrix
emat_rep2 <- ldat_new$spliced
emat_rep2 <- emat_rep2[, colnames(emat_rep2) %in% as.vector(rownames(meta_data))];
# intronic read (unspliced) expression matrix
nmat_rep2 <- ldat_new$unspliced
nmat_rep2 <- nmat_rep2[, colnames(nmat_rep2) %in% as.vector(rownames(meta_data))];
# spanning read (intron+exon) expression matrix
smat_rep2 <- ldat_new$spanning;
smat_rep2 <- smat_rep2[, colnames(smat_rep2) %in% as.vector(rownames(meta_data))];
######################
# Loading rep1 data
######################
ldat <- read.loom.matrices("/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/onefilepercell_P2221_N707-S505_and_others_96WOS.loom")
ldat <- lapply(ldat,function(x) {
colnames(x) <- gsub(".bam","",gsub(".*:","",colnames(x)))
x
})
WishBone_tsne <- read.csv(file="/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/WishBone_tsne_with_highTCR.csv", header=TRUE, sep=",")
colnames(WishBone_tsne) <- c('CellName','x','y','Sample')
colData <- read.csv(file="/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/colData.csv", header=TRUE, sep=",")
merged_meta1 <- merge(x = WishBone_tsne, y = colData, by = "CellName")
color_dt <- data.frame(Sample.x=c('WT1','WT2','WT3','WT4','WT5','highTCR_Rest'), color=c('#f44e42','#ffac38','#bcff37','#36eeff','#9335ff','#36ffee'))
merged_meta <- merge(x = merged_meta1, y = color_dt, by = "Sample.x")
merged_meta <- merged_meta[,c('CellCode','Sample.x')]
rownames(merged_meta) <- merged_meta$CellCode
merged_meta$rep <- 'rep1'
merged_meta <- merged_meta[,c('rep','Sample.x')]
colnames(merged_meta) <- c('rep','celltype')
merged_meta$celltype <- gsub('highTCR_Rest','TCRhiDP',gsub('WT5','CD8SP',gsub("WT4","CD4SP",gsub("WT3","CD4+8low",gsub("WT2","CD69posDP",gsub("WT1","CD69negDP",merged_meta$celltype))))))
# exonic read (spliced) expression matrix
emat_rep1 <- ldat$spliced
colnames(emat_rep1) <- gsub("-","_",colnames(emat_rep1))
emat_rep1 <- emat_rep1[, colnames(emat_rep1) %in% as.vector(rownames(merged_meta))];
# intronic read (unspliced) expression matrix
nmat_rep1 <- ldat$unspliced
colnames(nmat_rep1) <- gsub("-","_",colnames(nmat_rep1))
nmat_rep1 <- nmat_rep1[, colnames(nmat_rep1) %in% as.vector(rownames(merged_meta))];
# spanning read (intron+exon) expression matrix
smat_rep1 <- ldat$spanning;
colnames(smat_rep1) <- gsub("-","_",colnames(smat_rep1))
smat_rep1 <- smat_rep1[, colnames(smat_rep1) %in% as.vector(rownames(merged_meta))];
############
#merging rep1 & rep2
############
emat <- cbind(emat_rep1,emat_rep2)
nmat <- cbind(nmat_rep1,nmat_rep2)
smat <- cbind(smat_rep1,smat_rep2)
meta_info <- rbind(merged_meta,meta_data)
###########
# Running Seurat
###########
WT_Thymocye <- CreateSeuratObject(emat, meta.data = meta_info)
WT_Thymocye.list <- SplitObject(WT_Thymocye, split.by = "rep")
for (i in 1:length(WT_Thymocye.list)) {
WT_Thymocye.list[[i]] <- NormalizeData(WT_Thymocye.list[[i]], verbose = FALSE)
WT_Thymocye.list[[i]] <- FindVariableFeatures(WT_Thymocye.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
}
Rep1_log <- t(GetAssayData(WT_Thymocye.list[[1]],slot='data',assay = "RNA"))
Rep2_log <- t(GetAssayData(WT_Thymocye.list[[3]],slot='data',assay = "RNA"))
Rep2KO_log <- t(GetAssayData(WT_Thymocye.list[[2]],slot='data',assay = "RNA"))
meta_info$x1 <- 'Others'
meta_info$x2 <- 'Others'
meta_info$x3 <- 'Others'
meta_info$x4 <- 'Others'
meta_info$x5 <- 'Cd8-'
meta_info$x6 <- 'Cd4-'
meta_info$x7 <- 'Itm2a-'
meta_info$x8 <- 'Stat1-'
colnames(meta_info) <- c("rep", "celltype", "Cd4+Cd8+","Cd4+Cd8-", "Cd4-Cd8+", "Cd4-Cd8-", 'Cd8', 'Cd4', 'Itm2a', 'Stat1')
meta_info[rownames(Rep1_log[Rep1_log[,'Cd4'] > 0.1 & Rep1_log[,'Cd8a'] > 0.1,]),'Cd4+Cd8+'] <- 'Cd4+Cd8+'
meta_info[rownames(Rep2_log[Rep2_log[,'Cd4'] > 0.1 & Rep2_log[,'Cd8a'] > 0.1,]),'Cd4+Cd8+'] <- 'Cd4+Cd8+'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Cd4'] > 1 & Rep2KO_log[,'Cd8a'] > 1,]),'Cd4+Cd8+'] <- 'Cd4+Cd8+'
meta_info[rownames(Rep1_log[Rep1_log[,'Cd4'] > 0.1 & Rep1_log[,'Cd8a'] < 0.1,]),'Cd4+Cd8-'] <- 'Cd4+Cd8-'
meta_info[rownames(Rep2_log[Rep2_log[,'Cd4'] > 0.1 & Rep2_log[,'Cd8a'] < 0.1,]),'Cd4+Cd8-'] <- 'Cd4+Cd8-'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Cd4'] > 1 & Rep2KO_log[,'Cd8a'] < 1,]),'Cd4+Cd8-'] <- 'Cd4+Cd8-'
meta_info[rownames(Rep1_log[Rep1_log[,'Cd4'] < 0.1 & Rep1_log[,'Cd8a'] > 0.1,]),'Cd4-Cd8+'] <- 'Cd4-Cd8+'
meta_info[rownames(Rep2_log[Rep2_log[,'Cd4'] < 0.1 & Rep2_log[,'Cd8a'] > 0.1,]),'Cd4-Cd8+'] <- 'Cd4-Cd8+'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Cd4'] < 1 & Rep2KO_log[,'Cd8a'] > 1,]),'Cd4-Cd8+'] <- 'Cd4-Cd8+'
meta_info[rownames(Rep1_log[Rep1_log[,'Cd4'] < 0.1 & Rep1_log[,'Cd8a'] < 0.1,]),'Cd4-Cd8-'] <- 'Cd4-Cd8-'
meta_info[rownames(Rep2_log[Rep2_log[,'Cd4'] < 0.1 & Rep2_log[,'Cd8a'] < 0.1,]),'Cd4-Cd8-'] <- 'Cd4-Cd8-'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Cd4'] < 1 & Rep2KO_log[,'Cd8a'] < 1,]),'Cd4-Cd8-'] <- 'Cd4-Cd8-'
meta_info[rownames(Rep1_log[Rep1_log[,'Cd8a'] > 0.1,]),'Cd8'] <- 'Cd8+'
meta_info[rownames(Rep2_log[Rep2_log[,'Cd8a'] > 0.1,]),'Cd8'] <- 'Cd8+'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Cd8a'] > 0.1,]),'Cd8'] <- 'Cd8+'
meta_info[rownames(Rep1_log[Rep1_log[,'Cd4'] > 0.1,]),'Cd4'] <- 'Cd4+'
meta_info[rownames(Rep2_log[Rep2_log[,'Cd4'] > 0.1,]),'Cd4'] <- 'Cd4+'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Cd4'] > 0.1,]),'Cd4'] <- 'Cd4+'
meta_info[rownames(Rep1_log[Rep1_log[,'Itm2a'] > 0.5,]),'Itm2a'] <- 'Itm2a+'
meta_info[rownames(Rep2_log[Rep2_log[,'Itm2a'] > 0.5,]),'Itm2a'] <- 'Itm2a+'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Itm2a'] > 0.1,]),'Itm2a'] <- 'Itm2a+'
meta_info[rownames(Rep1_log[Rep1_log[,'Stat1'] > 0.2,]),'Stat1'] <- 'Stat1+'
meta_info[rownames(Rep2_log[Rep2_log[,'Stat1'] > 0.2,]),'Stat1'] <- 'Stat1+'
meta_info[rownames(Rep2KO_log[Rep2KO_log[,'Stat1'] > 0.1,]),'Stat1'] <- 'Stat1+'
meta_info[meta_info$celltype=='CD4SP' & meta_info$Itm2a=='Itm2a+' & meta_info$Stat1=='Stat1-', 'celltype'] <- 'CD4SP_Immature'
meta_info[meta_info$celltype=='CD4SP' & (meta_info$Itm2a=='Itm2a-' | meta_info$Stat1=='Stat1+'), 'celltype'] <- 'CD4SP_Mature'
WT_Thymocye <- CreateSeuratObject(emat, meta.data = meta_info)
WT_Thymocye.list <- SplitObject(WT_Thymocye, split.by = "rep")
for (i in 1:length(WT_Thymocye.list)) {
WT_Thymocye.list[[i]] <- NormalizeData(WT_Thymocye.list[[i]], verbose = FALSE)
WT_Thymocye.list[[i]] <- FindVariableFeatures(WT_Thymocye.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
}
meta_info <- WT_Thymocye@meta.data
reference.list <- WT_Thymocye.list[c("rep1", "rep2","rep2_KO")]
WT_Thymocye.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:30)
WT_Thymocye.integrated <- IntegrateData(anchorset = WT_Thymocye.anchors, dims = 1:30)
DefaultAssay(WT_Thymocye.integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
WT_Thymocye.integrated <- ScaleData(WT_Thymocye.integrated, verbose = FALSE)
WT_Thymocye.integrated <- RunPCA(WT_Thymocye.integrated, npcs = 30, verbose = FALSE)
WT_Thymocye.integrated <- RunUMAP(WT_Thymocye.integrated, reduction = "pca", dims = 1:30)
gene_set <- read.csv(file="/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/scRNAseq_CRG_02012020/gene_set/DE_gene_drodeRes_rep2_fset.csv", header=FALSE, sep=",")
emat <- cbind(emat_rep1,emat_rep2)
nmat <- cbind(nmat_rep1,nmat_rep2)
smat <- cbind(smat_rep1,smat_rep2)
# meta_info <- rbind(merged_meta,meta_data)
emat <- emat[intersect(gene_set$V1,rownames(emat)),]
emat <- emat[!(rownames(emat) =='Cd4' | rownames(emat) == 'Cd8a' | rownames(emat) =='Cd8b1'),]
###########
#Running Seurat
###########
WT_Thymocye <- CreateSeuratObject(emat, meta.data = meta_info)
WT_Thymocye.list <- SplitObject(WT_Thymocye, split.by = "rep")
for (i in 1:length(WT_Thymocye.list)) {
WT_Thymocye.list[[i]] <- NormalizeData(WT_Thymocye.list[[i]], verbose = FALSE)
WT_Thymocye.list[[i]] <- FindVariableFeatures(WT_Thymocye.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
}
reference.list <- WT_Thymocye.list[c("rep1", "rep2","rep2_KO")]
WT_Thymocye.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:30)
WT_Thymocye.integrated <- IntegrateData(anchorset = WT_Thymocye.anchors, dims = 1:30)
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(WT_Thymocye.integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
WT_Thymocye.integrated <- ScaleData(WT_Thymocye.integrated, verbose = FALSE)
WT_Thymocye.integrated <- RunPCA(WT_Thymocye.integrated, npcs = 30, verbose = FALSE)
WT_Thymocye.integrated <- RunUMAP(WT_Thymocye.integrated, reduction = "pca", dims = 1:30)
# Filtering cells with too many or too few cells
Rep1_filtered_rep2 <- subset(WT_Thymocye.integrated, cells = rownames(meta_info[meta_info$rep=='rep1' |
(meta_info$rep=='rep2_KO' & meta_info$celltype=='CD69posDP' & meta_info$nCount_RNA < 1500000 & meta_info$nCount_RNA > 750000) |
(meta_info$rep=='rep2_KO' & meta_info$celltype!='CD69posDP' & meta_info$nCount_RNA < 1500000 & meta_info$nCount_RNA > 500000) |
(meta_info$rep=='rep2' & meta_info$celltype=='CD69posDP' & meta_info$nCount_RNA < 1500000 & meta_info$nCount_RNA > 750000) |
(meta_info$rep=='rep2' & meta_info$celltype!='CD69posDP' & meta_info$nCount_RNA < 1500000 & meta_info$nCount_RNA > 500000)
,]))
# Dividing CD4SP to CD4SP Immature and Mature
meta_info_new <- Rep1_filtered_rep2@meta.data
PCA <- as.data.frame(Rep1_filtered_rep2@reductions$pca@cell.embeddings)
rownames(PCA) <- rownames(Rep1_filtered_rep2@reductions$pca@cell.embeddings)
meta_info_new$PC_1 <- PCA$PC_1
meta_info_new$PC_2 <- PCA$PC_2
meta_info_new$PC_3 <- PCA$PC_3
meta_info_new[meta_info_new$celltype=='CD4+8low' & meta_info_new$PC_2 < meta_info_new$PC_1,'celltype'] <- 'CD4SP_Immature'
meta_info[rownames(meta_info_new),'celltype'] <- meta_info_new$celltype
```
```{r,echo=T, eval=T, cache=T, message=F, warning=FALSE, fig.width=14, fig.height=3.5}
gene_set <- read.csv(file="/Volumes/bioinfomatics$/Mehdi/Matthias/scRNA-seq/scRNAseq_CRG_02012020/gene_set/DE_gene_drodeRes_rep2_fset.csv", header=FALSE, sep=",")
emat <- cbind(emat_rep1,emat_rep2)
nmat <- cbind(nmat_rep1,nmat_rep2)
smat <- cbind(smat_rep1,smat_rep2)
# meta_info <- rbind(merged_meta,meta_data)
emat <- emat[intersect(gene_set$V1,rownames(emat)),]
emat <- emat[!(rownames(emat) =='Cd4' | rownames(emat) == 'Cd8a' | rownames(emat) =='Cd8b1'),]
# gene_set <- gene_set[!(gene_set$V1 =='Cd4' | gene_set$V1 =='Cd8a' | gene_set$V1 =='Cd8b1'),]
###########
#Running Seurat
###########
WT_Thymocye <- CreateSeuratObject(emat, meta.data = meta_info)
WT_Thymocye.list <- SplitObject(WT_Thymocye, split.by = "rep")
for (i in 1:length(WT_Thymocye.list)) {
WT_Thymocye.list[[i]] <- NormalizeData(WT_Thymocye.list[[i]], verbose = FALSE)
WT_Thymocye.list[[i]] <- FindVariableFeatures(WT_Thymocye.list[[i]], selection.method = "vst",
nfeatures = 2000, verbose = FALSE)
}
reference.list <- WT_Thymocye.list[c("rep1", "rep2", "rep2_KO")]
WT_Thymocye.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:30)
WT_Thymocye.integrated <- IntegrateData(anchorset = WT_Thymocye.anchors, dims = 1:30)
library(ggplot2)
library(cowplot)
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(WT_Thymocye.integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
WT_Thymocye.integrated <- ScaleData(WT_Thymocye.integrated, verbose = FALSE)
WT_Thymocye.integrated <- RunPCA(WT_Thymocye.integrated, npcs = 30, verbose = FALSE)
WT_Thymocye.integrated <- RunUMAP(WT_Thymocye.integrated, reduction = "pca", dims = 1:30)
PCA <- as.data.frame(WT_Thymocye.integrated@reductions$pca@cell.embeddings)
# Original runing
emat <- cbind(emat_rep1,emat_rep2)
nmat <- cbind(nmat_rep1,nmat_rep2)
smat <- cbind(smat_rep1,smat_rep2)
###########
#Running Seurat
###########
WT_Thymocye <- CreateSeuratObject(emat, meta.data = meta_info)
WT_Thymocye.list <- SplitObject(WT_Thymocye, split.by = "rep")
for (i in 1:length(WT_Thymocye.list)) {
WT_Thymocye.list[[i]] <- NormalizeData(WT_Thymocye.list[[i]], verbose = FALSE)
WT_Thymocye.list[[i]] <- FindVariableFeatures(WT_Thymocye.list[[i]], selection.method = "vst",
nfeatures = 5000, verbose = FALSE)
}
reference.list <- WT_Thymocye.list[c("rep1", "rep2", "rep2_KO")]
WT_Thymocye.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:30)
WT_Thymocye.integrated <- IntegrateData(anchorset = WT_Thymocye.anchors, dims = 1:30)
library(ggplot2)
library(cowplot)
# switch to integrated assay. The variable features of this assay are automatically
# set during IntegrateData
DefaultAssay(WT_Thymocye.integrated) <- "integrated"
WT_Thymocye.integrated <- ScaleData(WT_Thymocye.integrated, verbose = FALSE)
WT_Thymocye.integrated <- RunPCA(WT_Thymocye.integrated, npcs = 30, verbose = FALSE)
WT_Thymocye.integrated <- RunUMAP(WT_Thymocye.integrated, reduction = "pca", dims = 1:30)
WT_Thymocye.integrated@reductions$pca@cell.embeddings <- as.matrix(PCA)
Rep1_filtered_rep2 <- subset(WT_Thymocye.integrated, cells = rownames(meta_info[
(meta_info$rep=='rep2' & meta_info$celltype=='CD69posDP' & meta_info$nCount_RNA < 1500000 & meta_info$nCount_RNA > 750000) |
(meta_info$rep=='rep2' & meta_info$celltype!='CD69posDP' & meta_info$nCount_RNA < 1500000 & meta_info$nCount_RNA > 500000)
,]))
DefaultAssay(Rep1_filtered_rep2) <- "RNA"
Rep1_filtered_rep2@meta.data$CD4_expression <- 0
Rep1_filtered_rep2@meta.data$CD4_expression <- t(GetAssayData(Rep1_filtered_rep2,slot='data',assay = "RNA"))[,"Cd4"]
Rep1_filtered_rep2@meta.data$Itm2a_expression <- 0
Rep1_filtered_rep2@meta.data$Itm2a_expression <- t(GetAssayData(Rep1_filtered_rep2,slot='data',assay = "RNA"))[,"Itm2a"]
Rep1_filtered_rep2@meta.data$Tbx21_expression <- 0
Rep1_filtered_rep2@meta.data$Tbx21_expression <- t(GetAssayData(Rep1_filtered_rep2,slot='data',assay = "RNA"))[,"Tbx21"]
draw_FeaturePlot <- function(pair) {
P1 <- FeaturePlot(Rep1_filtered_rep2,features = pair, slot='data',label = F, blend = TRUE, order = TRUE, reduction = "pca", pt.size=1, cols = c("darkgrey", "#ff0000", "#00ff00"), blend.threshold = 0.1)
P2 <- FeaturePlot(Rep1_filtered_rep2,features = pair, slot='data',label = F, blend = TRUE, order = TRUE, reduction = "pca", dims = c(1,3), pt.size=1, cols = c("darkgrey", "#ff0000", "#00ff00"), blend.threshold = 0.1)
print(P1)
print(P2)
}
```
## Figure 2: scRNA-seq captures maturation, activation, and lineage identity as principal components of CD4/CD8 lineage choice and differentiation
```{r,echo=T, eval=T, cache=T, message=F, warning=FALSE, fig.width=14, fig.height=3.5}
draw_FeaturePlot(c("Rag1","Ccr7"))
draw_FeaturePlot(c("Cd69","Cd69"))
draw_FeaturePlot(c("CD4_expression","Cd8a"))
```
## Figure 6.ab) CD4 and CD8 lineage characteristics are established at different times during CD4/CD8 lineage choice and differentiation
```{r,echo=T, eval=T, cache=T, message=F, warning=FALSE, fig.width=14, fig.height=3.5}
draw_FeaturePlot(c("Itm2a_expression","Zbtb7b"))
draw_FeaturePlot(c("Itm2a_expression","Cd40lg"))
draw_FeaturePlot(c("Itm2a_expression","Nkg7"))
draw_FeaturePlot(c("Itm2a_expression","Itgae"))
```
## Sup Figure 2.bcde) Key genes and correlations
```{r,echo=T, eval=T, cache=T, message=F, warning=FALSE, fig.width=14, fig.height=3.5}
draw_FeaturePlot(c("Rag1","Dntt"))
draw_FeaturePlot(c("Rag1","Cd69"))
draw_FeaturePlot(c("Ccr7","Il7r"))
draw_FeaturePlot(c("CD4_expression","Cd8a"))
```
## Session Info
```{r Session, eval=T, echo=T}
sessionInfo()
```