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Extended-Data-Figure_8.R
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Extended-Data-Figure_8.R
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library(KernSmooth)
library(NMF)
library(rsvd)
library(Rtsne)
library(ggplot2)
library(cowplot)
library(sva)
library(igraph)
library(cccd)
library(destiny)
library(stringr)
library(reshape2)
source('Fxns.R')
RANKL_UMIs<-load_data(data_name = './data/GSE92332_Org_RANKL_UMIcounts.txt.gz')
RANKL_UMIs<-RANKL_UMIs[which(unlist(apply(RANKL_UMIs,1,sum))>0),]
v = get.variable.genes(RANKL_UMIs, min.cv2 = 100)
var.genes = as.character(rownames(v)[v$p.adj<0.05]) # select genes
RANKL_tpm = data.frame(log2(1+tpm(RANKL_UMIs)))
RANKL.all.cells<-colnames(RANKL_UMIs)
RANKL.all.genes<-rownames(RANKL_UMIs)
RANKL.condition<-unlist(lapply(RANKL.all.cells,function(x)return(str_split(x,'_')[[1]][3])))
RANKL.cell.groups<-unlist(lapply(RANKL.all.cells,function(x)return(str_split(x,'_')[[1]][4])))
RANKL.batches<-unlist(lapply(RANKL.all.cells,function(x)return(str_split(x,'_')[[1]][1])))
table(RANKL.batches)
batch_mean_tpm = group.means(counts = RANKL_tpm, groups = RANKL.batches)
x = batch_mean_tpm[, 1]
y = batch_mean_tpm[,2]
expr.cor = round(cor(x,y),2)
smoothScatter(x, y, nrpoints=Inf, pch=16, cex=0.25, main=sprintf("Before batch correction, correlation between \ntwo illustrative batches is %s", expr.cor), xlab="All genes Batch 2, mean log2(TPM+1)", ylab="All genes Batch 1, mean log2(TPM+1)")
RANKL_tpm_norm = batch.normalise.comBat(counts = as.matrix(RANKL_tpm), batch.groups = RANKL.batches)
batch_mean_tpm_norm = group.means(counts =RANKL_tpm_norm, groups = RANKL.batches)
x = batch_mean_tpm_norm[, 1]
y = batch_mean_tpm_norm[,2]
expr.cor = round(cor(x,y),2)
smoothScatter(x, y, nrpoints=Inf, pch=16, cex=0.25, main=sprintf("After batch correction, correlation between \ntwo illustrative batches is %s", expr.cor), xlab="All genes Batch 2, mean log2(TPM+1)", ylab="All genes Batch 1, mean log2(TPM+1)")
### TSNE
RANKL.tsne.rot<-PCA_TSNE.scores(data.tpm = RANKL_tpm_norm,data.umis = RANKL_UMIs,var_genes = var.genes,
data_name = './data/Org_RANK',sig.pcs = TRUE,is.var.genes = TRUE)
RANKL.tsne.rot<-data.frame(RANKL.tsne.rot)
colnames(RANKL.tsne.rot)<-c('tSNE_1','tSNE_2')
### Figure a
#control.color<-ifelse(RANKL.condition=='Control','red','white')
ggplot(data = RANKL.tsne.rot[RANKL.condition=='Control',],aes(x=tSNE_1,y=tSNE_2))+geom_point()+
scale_color_manual(values=brewer16)+scale_fill_discrete()+theme(legend.title=element_blank())+ggtitle('Org RANKL Control')
ggplot(data = RANKL.tsne.rot[RANKL.condition=="RANKL.Day3",],aes(x=tSNE_1,y=tSNE_2))+geom_point()+
scale_color_manual(values=brewer16)+scale_fill_discrete()+theme(legend.title=element_blank())+ggtitle('Org RANKL Day3')
ggplot(data = RANKL.tsne.rot[RANKL.condition=="RANKL.Day6",],aes(x=tSNE_1,y=tSNE_2))+geom_point()+
scale_color_manual(values=brewer16)+scale_fill_discrete()+theme(legend.title=element_blank())+ggtitle('Org RANKL Day6')
### Figure b
Tnfaip2<-Genes_mean_tpm(genes = 'Tnfaip2',tpm_data = RANKL_tpm_norm,tsne_data = RANKL.tsne.rot,title = 'M-sec')
### Figure c
Gp2<-Genes_mean_tpm(genes = 'Gp2',tpm_data = RANKL_tpm_norm,tsne_data = RANKL.tsne.rot,title =NULL)
### Figure d
library(vioplot)
RANKL.marker.genes<-c('Anxa5','Ccl20','Ccl6','Ccl9','Gp2','Marcksl1','Pglyrp1','Rac2',
'Spib','Tnfaip2')
# for(g in RANKL.marker.genes){
# if(!g%in%RANKL.all.genes){
# cat(sprintf('%s\n',g))
# }
# }
# RANKL.marker.tpm<-data.frame(t(RANKL_tpm_norm[RANKL.marker.genes,]))
# RANKL.marker.tpm$Groups<-RANKL.cell.groups
#
# RANKL.marker.melt.tpm<-melt(RANKL.marker.tpm)
# p<-ggplot(data = RANKL.marker.melt.tpm,aes(x=Groups,y=value))+geom_violin(aes(colour=variable,fill=value),trim =TRUE)
# p<-p+theme(axis.text.x = element_text(size=10,angle = 90,face = 'bold'))
# p
Groups.color<-ifelse(RANKL.cell.groups=='M.cell','blue','red')
Violin_Genes<-function(gene,tpm.data,cell.groups,marker.genes,groups.color){
x<-data.frame(t(tpm.data[marker.genes,]))
dat<-data.frame(x[,gene])
dat$Groups<-cell.groups
colnames(dat)<-c(gene,'Groups')
print(ggplot(data = dat,aes(x=dat[,2],y=dat[,1]))+geom_violin(aes(colour=Groups.color))+coord_flip()+
geom_jitter(shape=16, position=position_jitter(0.1),size=1)+theme(legend.title = element_blank(),legend.position = 'none',
axis.text.y = element_text(colour = c(rep('blue',6),'red',rep('blue',6))))+ylab(gene)+xlab(NULL))
}
# Violin_Genes(gene = 'Ccl20',tpm.data = RANKL_tpm_norm,cell.groups = RANKL.cell.groups,marker.genes = RANKL.marker.genes,
# groups.color = Groups.color)
# for(gene in RANKL.marker.genes){
# Violin_Genes(gene = gene,tpm.data = RANKL_tpm_norm,cell.groups = RANKL.cell.groups,marker.genes = RANKL.marker.genes,
# groups.color = Groups.color)
#
# }
RANKL.marker.tpm<-data.frame(t(RANKL_tpm_norm[RANKL.marker.genes,]))
RANKL.marker.tpm$Cells<-RANKL.cell.groups
RANKL.marker.tpm.melt<-melt(RANKL.marker.tpm)
ggplot(data=RANKL.marker.tpm.melt,aes(x=Cells,y=value))+
geom_violin(aes(fill=value,colour=Cells))+
geom_jitter(shape=16,size=0.2, position=position_jitter(width = 0.2))+
facet_wrap(~ variable, nrow = 10)+
xlab(NULL)+ylab('Expression\n(Log2(TPM+1)')+
theme(axis.text.x = element_text(size=10,angle = 90,face = 'bold',
colour = c(rep('blue',6),'red',rep('blue',6))))
### Figure e
#### heatmap
heatmap.cells.e<-c("Endocrine","Tuft","Enterocyte.1","Stem","Goblet","M.cell")
heatmap.genes.e<-c('Aif1','Adm','Bcl2a1b','Bcl2a1a','C1qtnf6','Fam131a','H2-M2','Msln','Padi2',
'Psg27','Psg25','Rassf2','Rps6kl1','Sox8','Tnfrsf11b','Tnfrsf4','Ccl9','Anxa5',
'Spib','Ctsh','Fabp5','Ccl20','Pglyrp1','Rac2','Gjb2','Marcksl1','Bcl2a1d','Gp2',
'Slc2a6','Pold1','Ubd','Mfge8','Rab32','Myadm','Frmd4b','Ncf4','Cyba','St5','Tmprss2',
'Epb41l2','Mmp15','Far2','Gfer','Zmat3')
heatmap.e<-Create_plot_data(genes = heatmap.genes.e,origin.data = RANKL_tpm_norm,cell_groups = RANKL.cell.groups,
cells = heatmap.cells.e)
p<-ggplot(heatmap.e,aes(x=Groups,y=variable))+geom_tile(aes(fill=value))+scale_fill_gradient2(mid ='grey',high='red',name='Mean\nLog2(TPM+1)')
#scale_fill_continuous(low='lightblue',high='red')+theme_bw()
p+labs(x='',y='')+scale_x_discrete(expand = c(0,0),position = 'bottom')+
scale_y_discrete(expand = c(0,0),position = 'right')+theme(axis.text.x = element_text(face="bold", color=c("red"), size=10,hjust = 1,angle = 90),
axis.text.y = element_text(size=8),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_blank(),
axis.line = element_line(colour = "black"))
### Figure f
heatmap.cells.f<-c("Endocrine","Tuft","Enterocyte.1","Stem","Goblet","M.cell")
heatmap.genes.f<-c('Ahr','Mtf1','Relb','Mier3','Sox8','Tulp4','Onecut2','Fosl2',
'Irf6','Irf2','Zfp553','Nfib','Spib','Foxp4')
heatmap.f<-Create_plot_data(genes = heatmap.genes.f,origin.data = RANKL_tpm_norm,cell_groups = RANKL.cell.groups,
cells = heatmap.cells.f)
p<-ggplot(heatmap.f,aes(x=Groups,y=variable))+geom_tile(aes(fill=value))+scale_fill_gradient2(mid ='grey',high='red',name='Mean\nLog2(TPM+1)')
#scale_fill_continuous(low='lightblue',high='red')+theme_bw()
p+labs(x='',y='')+scale_x_discrete(expand = c(0,0),position = 'bottom')+
scale_y_discrete(expand = c(0,0),position = 'right')+theme(axis.text.x = element_text(face="bold", color=c("red"), size=10,hjust = 1,angle = 90),
axis.text.y = element_text(size=8),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.border = element_blank(),
axis.line = element_line(colour = "black"))
### Figure g
library(vioplot)
M.cells<-RANKL_tpm_norm[var.genes,RANKL.cell.groups%in%'M.cell']
All.cells<-RANKL_tpm_norm[var.genes,]
M.cells.mean<-unlist(apply(M.cells,2,mean))
All.cells.mean<-unlist(apply(All.cells,2,mean))
vioplot(All.cells.mean,M.cells.mean,names = c('All.cells','M.cells'),col = c('pink','red'))
title(ylab = 'ln vitro M cell signature\nLog2(TPM+1)')