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11Marquers.R
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11Marquers.R
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rm(list = ls())
#Did you change it to your base location?
baseDir="~/LeadNPC/"
setwd(baseDir)
source(file = "bin/00base.R")
layout="col"
normalize = F
# Ler tabela de counts ----------------------------------------------------
figures="figures"
graficos="graficos"
load(file = "./Data/counts.RData")
#create biomart list of association
# library('biomaRt')
# mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
# #searchFilters(mart = mart, pattern = "ensembl.*id")
# genes <- rownames(logCPM)
#
# G_list <- getBM(filters= "ensembl_gene_id",
# attributes= c("ensembl_gene_id","hgnc_symbol"),
# mart= mart,
# values = genes)
#save(G_list,file = "./Data/Glist.RData")
load(file = "./Data/Glist.RData")
load(file = "./Data/counts.RData")
logCPM<-as.data.frame(logCPM)
logCPM$ensg<-rownames(logCPM)
logCPM<-merge(logCPM,G_list,by.x="ensg",by.y="ensembl_gene_id")
#duplicate day 0 to treated group
day0<-pheno_data[pheno_data$Day==0,]
day0$Group<-"Lead30"
pheno_data<-rbind(pheno_data,day0)
# ,"VIM" Radial glial cell markers
#"MAPT", Não achei
#"CXCR4", tronco para neural
# ncp<-c("PAX6","MSI1","NES","NOTCH1","SOX1","SOX2","FUT4")
# neuron<-c("TH","NEUROD6","DCX", "RBFOX3","BCL11B","SOX11","SOX4")
#dopaminergic markers TH, ,"LMX1B"
ncp<-c("MSI1","NES","SOX1","NOTCH1")
neuron<-c("TH","NEUROD6","DCX", "RBFOX3","GAD1","GAD2")
#to create color palette
# library(wesanderson)
# color=c(wes_palette("Rushmore1")[c(1,3,5)],
# #wes_palette("Royal2"),
# wes_palette("Royal1")[c(1)],
# "#0000FF","#00FF00","#FF8C00",
# wes_palette("BottleRocket2")[1:4])
#colourpicker::colourPicker(11)
# ggplot(colors, aes(factor(gene), fill=color)) +
# geom_bar() +
# scale_fill_manual(values = colors$color)
color<-c("#E1BD6D","#228B22","#F0800F","#899DA4","#0000FF","#00FF00","#8A2BE2","#FAD510","#CB2314","#00CDCD")
colors<-data.frame(gene=c(ncp,neuron),
color=color,
stringsAsFactors = F)
xcolors <- setNames(colors$color, colors$gene)
logCPM$type[logCPM$hgnc_symbol%in%ncp]<-1
logCPM$type[logCPM$hgnc_symbol%in%neuron]<-2
ctlExp<-as.data.frame(logCPM[logCPM$type%in%c(1,2),c(2:56)])
vgroup=c("Control","Lead30")
# vbase<-list(ctlExp[,c("SRR3944315","hgnc_symbol")],
# ctlExp[,c("SRR3944352","hgnc_symbol")])
vbase<-list(ctlExp[,c("SRR3944315","hgnc_symbol")],
ctlExp[,c("SRR3944315","hgnc_symbol")])
library(tidyr)
treat=1
g<-list()
gIndex=1
maxyMean<-0
maxyMean2<-0
#loop ----
for(treat in c(1,2)){
group<-vgroup[treat]
base<-vbase[[treat]]
# PAX6, SOX1
# NEUROD6,RBFOX3, DCX,
ctlExp2<-gather(ctlExp,key,value,-54:-55)
ctlExp2$value<-as.numeric(ctlExp2$value)
ctlExp2<-ctlExp2[ctlExp2$key%in%pheno_data$Run[pheno_data$Group ==group],]
control<-pheno_data[pheno_data$Group == group,]
control<-control[order(control$Day),]
colnames(base)<-c("base","hgnc_symbol")
ctlExp2<- merge(ctlExp2,control[,c("Run","Day")],
by.x = "key",
by.y= "Run")
ctlExp2<-merge(ctlExp2,base, by="hgnc_symbol")
if(normalize){
ctlExp2$value0<-(ctlExp2$value/ctlExp2$base)
}else{
ctlExp2$value0<-ctlExp2$value
}
ctlExp2<-ctlExp2[order(ctlExp2$Day),]
ctlExp2<-merge(ctlExp2,
colors,
by.x = "hgnc_symbol",
by.y = "gene")
ctlExp2$maxX<-26
library(ggplot2)
library(ggrepel)
miny<-min(ctlExp2$value0[ctlExp2$type==1])
maxy<-max(ctlExp2$value0[ctlExp2$type==1])
if(normalize){
timeLabels<-data.frame(label=c("Time-interval 1","Time-interval 2"),
x=c((11-3)/2+3,(26-11)/2+11),
y=rep((maxy-miny)*0.7+miny,2))
}else{
timeLabels<-data.frame(label=c("Time-interval 1","Time-interval 2"),
x=c((11-3)/2+3,(26-11)/2+11),
y=rep(1.25,2))
}
#plot 1----
maxyMean2<-max(maxyMean2,max(ctlExp2$value0))
g[[gIndex]]<-ggplot()+theme_bw()+
ggtitle(paste("NPC -",ifelse(treat==1,"Control","Treated")))+
xlab("Days")+
ylab(ifelse(normalize,"Relative expression","Expression"))
if(normalize){
g[[gIndex]]<-g[[gIndex]]+
geom_rect(aes_(xmin=3,
xmax=11.5,
ymin=miny,
ymax=maxy),
fill="#fbf2cc60")+
geom_rect(aes_(xmin=11.5,
xmax=26,
ymin=miny,
ymax=maxy),
fill="#cce3f260")
}else{
g[[gIndex]]<-g[[gIndex]]+
geom_rect(aes_(xmin=3,
xmax=11.5,
ymin=0,
ymax=maxyMean2),
fill="#fbf2cc60")+
geom_rect(aes_(xmin=11.5,
xmax=26,
ymin=0,
ymax=maxyMean2),
fill="#cce3f260")+
scale_y_continuous(labels = scales::number_format(accuracy = 0.1,
decimal.mark = '.'),
limits = c(0,maxyMean2))
}
g[[gIndex]]<-g[[gIndex]]+geom_text(data = timeLabels,
aes(x,y, label=label),
col=alpha(colour = "black",alpha = 0.25))+
geom_line(data=ctlExp2[ctlExp2$type==1&
ctlExp2$hgnc_symbol!="PAX6",],
aes(x=Day,
y=value0,
color=hgnc_symbol))+
scale_color_manual(values = xcolors)+
guides(colour = "none", linetype="none")+
geom_text_repel(data = ctlExp2[ctlExp2$type==1&
ctlExp2$Day==26,],
aes(x=Day,
y=value0,
label=hgnc_symbol,
#hjust = "left",
color=hgnc_symbol),
segment.size = .1,
nudge_x = .1 ,
cex=3)
gIndex<-gIndex+1
miny<-min(ctlExp2$value0[ctlExp2$type==2])
maxy<-max(ctlExp2$value0[ctlExp2$type==2])
if(normalize){
timeLabels<-data.frame(label=c("Time-interval 1","Time-interval 2"),
x=c((11-3)/2+3,(26-11)/2+11),
y=rep((maxy-miny)*0.7+miny,2))
}else{
timeLabels<-data.frame(label=c("Time-interval 1","Time-interval 2"),
x=c((11-3)/2+3,(26-11)/2+11),
y=rep(1.25,2))
}
textLabels<-ctlExp2[ctlExp2$type==2&
ctlExp2$Day==26,
c("hgnc_symbol","value0")]
textLabels$x<-26
maxyMean2<-max(maxyMean2,max(ctlExp2$value0))
#plot 2 ----
g[[gIndex]]<-ggplot()+theme_bw()+
ggtitle(paste("Neuron -",ifelse(treat==1,"Control","Treated")))+
xlab("Days")+
ylab(ifelse(normalize,"Relative expression","Expression"))+
geom_rect(aes_(xmin=3,
xmax=11.5,
ymin=miny,
ymax=maxy),
fill="#fbf2cc60")
if(normalize){
g[[gIndex]]<-g[[gIndex]]+geom_rect(aes_(xmin=11.5,
xmax=26,
ymin=miny,
ymax=maxy),
fill="#cce3f260")
}else{
g[[gIndex]]<-g[[gIndex]]+
geom_rect(aes_(xmin=3,
xmax=11.5,
ymin=0,
ymax=maxyMean2),
fill="#fbf2cc60")+
geom_rect(aes_(xmin=11.5,
xmax=26,
ymin=0,
ymax=maxyMean2),
fill="#cce3f260")+
scale_y_continuous(labels = scales::number_format(accuracy = 0.1,
decimal.mark = '.'),
limits = c(0,maxyMean2))
}
g[[gIndex]]<-g[[gIndex]]+geom_text(data = timeLabels,
aes(x,y, label=label),
col=alpha(colour = "black",alpha = 0.25))+
geom_line(data=ctlExp2[ctlExp2$type==2,],
aes(x=Day,
y=value0,
color=hgnc_symbol))+
scale_color_manual(values = xcolors)+
guides(colour = "none", linetype="none")+
geom_text_repel(data = ctlExp2[ctlExp2$type==2&
ctlExp2$Day==26,],
aes(x=Day,
y=value0,
label=hgnc_symbol,
#hjust = "left",
color=hgnc_symbol),
segment.size = .1,
nudge_x = .1 ,
cex=3)
gIndex<-gIndex+1
library(dplyr)
#sem grupo 0 ----
ctlExp2$TI<-ifelse(ctlExp2$Day%in%(0:11),1,2)
ctlTime<-ctlExp2[ctlExp2$Day>=0,]
genes<-unique(ctlTime$hgnc_symbol)
i="NOTCH1"
result<-data.frame(gene=character(),
pval=numeric(),
type=numeric(),
stringsAsFactors = F)
for (i in genes) {
tmp1<-ctlTime$value[ctlTime$hgnc_symbol==i&
ctlTime$TI==1]
tmp2<-ctlTime$value[ctlTime$hgnc_symbol==i&
ctlTime$TI==2]
result[nrow(result)+1,"gene"]<-i
result[nrow(result),"pval"]<-t.test(tmp1,tmp2)$p.value
result[nrow(result),"type"]<-unique(ctlTime$type[ctlTime$hgnc_symbol==i])
}
result$pCor<-p.adjust(result$pval,method = "BH")
result[result$pCor>0.01,]
ctTime<-ctlTime%>%
group_by(TI,type,hgnc_symbol)%>%
summarise(value=mean(value))
maxyMean <-max(maxyMean ,max(ctTime$value))
ctTime$type[ctTime$type==1]<-"NPC"
ctTime$type[ctTime$type==2]<-"Neuron"
ctTime<- merge(ctTime,result, by.x ="hgnc_symbol",
by.y = "gene")
ctTime$line<-1
ctTime$line[ctTime$pCor>0.01]<-2
ctTime$type<-factor(ctTime$type.x,levels=c("NPC","Neuron"))
#plot 3 ----
g[[gIndex]]<-ggplot()+theme_bw()+
ggtitle(ifelse(treat==1,"Control","Treated"))+
xlab("Time Interval")+
ylab("Mean expression")
if(layout=="col"){
g[[gIndex]]<-g[[gIndex]]+
facet_grid(type~.)
}else{
g[[gIndex]]<-g[[gIndex]]+
facet_grid(.~type)
}
g[[gIndex]]<-g[[gIndex]]+
geom_point(data=ctTime,
aes(x=as.factor(TI),
y=value,
group=hgnc_symbol,
color=factor(hgnc_symbol)))+
geom_line(data=ctTime,
aes(x=as.factor(TI),
y=value,
group=hgnc_symbol,
linetype=as.factor(line),
color=factor(hgnc_symbol)))+
geom_text_repel(data = ctTime[ctTime$TI==2,],
aes(x=as.factor(TI),
y=value,
label=hgnc_symbol,
hjust = "left",
color=factor(hgnc_symbol)),
segment.size = .1,
nudge_x = .1 )+
scale_y_continuous(labels = scales::number_format(accuracy = 0.1,
decimal.mark = '.'),
limits = c(0,maxyMean))+
guides(colour = "none", linetype="none")+
scale_color_manual(values = xcolors)
gIndex<-gIndex+1
# com grupo 0 ----
ctlExp2$TI<-0
ctlExp2$TI[ctlExp2$Day%in%(3:11)]<-1
ctlExp2$TI[ctlExp2$Day%in%(12:26)]<-2
ctlTime<-ctlExp2[ctlExp2$Day>=0,]
genes<-unique(ctlTime$hgnc_symbol)
i="GAD2"
result<-data.frame(gene=character(),
pval0to1=numeric(),
pval0to2=numeric(),
pval1to2=numeric(),
type=numeric(),
stringsAsFactors = F)
for (i in genes) {
tmp<-ctlTime[ctlTime$hgnc_symbol==i,
c("TI","value")]
tmp$TI<-paste0("T",tmp$TI)
#anova(ctlTime)
#aov(value ~ TI, data = tmp)
pairTest<-pairwise.t.test(tmp$value,tmp$TI,p.adjust.method = "BH")
result[nrow(result)+1,"gene"]<-i
result[nrow(result),"pval0to1"]<-pairTest$p.value[1,1]
result[nrow(result),"pval0to2"]<-pairTest$p.value[2,1]
result[nrow(result),"pval1to2"]<-pairTest$p.value[2,2]
result[nrow(result),"type"]<-unique(ctlTime$type[ctlTime$hgnc_symbol==i])
}
# result$pCor1<-p.adjust(result$pval1,method = "BH")
# result$pCor2<-p.adjust(result$pval2,method = "BH")
# result[result$pCor>0.01,]
# library(reshape2)
# melt(result)
ctTime<-ctlTime%>%
group_by(TI,type,hgnc_symbol)%>%
summarise(value=mean(value))
maxyMean <-max(maxyMean ,max(ctTime$value))
ctTime$type[ctTime$type==1]<-"NPC"
ctTime$type[ctTime$type==2]<-"Neuron"
ctTime<- merge(ctTime,result, by.x ="hgnc_symbol",
by.y = "gene")
ctTime$line0to1<-1
ctTime$line0to1[ctTime$pval0to1>0.01]<-2
ctTime$line1to2<-1
ctTime$line1to2[ctTime$pval1to2>0.01]<-2
ctTime$line0to2<-1
ctTime$line0to2[ctTime$pval0to2>0.01]<-0
#plot 4 ----
ctTime$type<-factor(ctTime$type.x,levels=c("NPC","Neuron"))
g[[gIndex]]<-ggplot()+theme_bw()+
ggtitle(ifelse(treat==1,"Control","Treated"))+
xlab("Time Interval")+
ylab("Mean expression")
if(layout=="col"){
g[[gIndex]]<-g[[gIndex]]+
facet_grid(type~.)
}else{
g[[gIndex]]<-g[[gIndex]]+
facet_grid(.~type)
}
g[[gIndex]]<-g[[gIndex]]+
geom_point(data=ctTime,
aes(x=as.factor(TI),
y=value,
group=hgnc_symbol,
color=factor(hgnc_symbol)),
pch=20,cex = 1)+
geom_line(data=ctTime[ctTime$TI%in%c(0:1),],
aes(x=as.factor(TI),
y=value,
group=hgnc_symbol,
linetype=as.factor(line0to1),
color=factor(hgnc_symbol)))+
geom_line(data=ctTime[ctTime$TI%in%c(1:2),],
aes(x=as.factor(TI),
y=value,
group=hgnc_symbol,
linetype=as.factor(line1to2),
color=factor(hgnc_symbol)))+
geom_point(data=ctTime[ctTime$TI%in%c(0,2)&
ctTime$line0to2==1,],
aes(x=as.factor(TI),
y=value,
group=hgnc_symbol,
color=factor(hgnc_symbol)),
pch = 15,cex=3)+
geom_text_repel(data = ctTime[ctTime$TI==2,],
aes(x=as.factor(TI),
y=value,
label=hgnc_symbol,
hjust = "left",
color=factor(hgnc_symbol)),
segment.size = .1,
nudge_x = .1,cex=6 )+
scale_y_continuous(labels = scales::number_format(accuracy = 0.1,
decimal.mark = '.'),
limits = c(0,maxyMean))+
guides(colour = "none", linetype="none")+
scale_color_manual(values = xcolors)
gIndex<-gIndex+1
ctlExp2$treat<-group
if(!exists("allTreat")){
allTreat<-ctlExp2
}else{
allTreat<-rbind(allTreat,ctlExp2)
}
}
ggsave(filename = "./figures/markControl.pdf",
plot = g[[4]],
device = "pdf",
#scale = 2.5,
width = 7, height = 4.25, units = "in",
dpi = 300)
ggsave(filename = "./figures/markTreat.pdf",
plot = g[[8]],
device = "pdf",
#scale = 2.5,
width = 7, height = 4.25, units = "in",
dpi = 300)
#painel ----
#painel 1----
genes1<-unique(allTreat$hgnc_symbol[allTreat$type==1])
painel1<-list()
indice<-1
for(indice in 1:length(genes1)){
maxy1<-max(allTreat$value0[allTreat$hgnc_symbol==genes1[indice]])
kst<-allTreat[allTreat$hgnc_symbol==genes1[indice]&
allTreat$Day>0,]
d1<-kst[kst$treat=="Control"&
kst$Day%in%c(3:11),]
d1<-d1$value0[order(d1$Day)]
d2<-kst[kst$treat=="Lead30"&
kst$Day%in%c(3:11),]
d2<-d2$value0[order(d2$Day)]
ks1<-ks.test(d1,d2, alternative = "t")
d1<-kst[kst$treat=="Control"&
kst$Day%in%c(12:26),]
d1<-d1$value0[order(d1$Day)]
d2<-kst[kst$treat=="Lead30"&
kst$Day%in%c(12:26),]
d2<-d2$value0[order(d2$Day)]
ks2<-ks.test(d1,d2, alternative = "t")
timeLabels<-data.frame(label=c("Time-interval 1","Time-interval 2"),
x=c((11-3)/2+3,(26-11)/2+11),
y=rep(maxy1/10,2))
ks1<-p.adjust(c(ks1$p.value,ks2$p.value),
method = "BH")
pval1<-paste0("p-val: ",sprintf("%1.2E",ks1[1]))
pval2<-paste0("p-val: ",sprintf("%1.2E",ks1[2]))
painel1[[indice]]<-ggplot()+theme_bw()+
ggtitle(genes1[indice])+
xlab("Days")+
ylab("Expression")+
geom_rect(aes_(xmin=3,
xmax=11.5,
ymin=0,
ymax=maxy1),
fill="#fbf2cc60")+
geom_rect(aes_(xmin=11.5,
xmax=26,
ymin=0,
ymax=maxy1),
fill="#cce3f260")+
scale_y_continuous(labels = scales::number_format(accuracy = 0.1,
decimal.mark = '.'))+
geom_text(data = timeLabels,
aes(x,y, label=label),
col=alpha(colour = "black",alpha = 0.25))+
geom_text(aes_(x=7,y=(maxy1/4),
label=pval1),
cex=3,
col=ifelse(ks1[1]<=0.01,"blue","red"))+
geom_text(aes_(x=19,y=(maxy1/4),
label=pval2),
cex=3,
col=ifelse(ks1[2]<=0.01,"blue","red"))+
geom_line(data=allTreat[allTreat$hgnc_symbol==genes1[indice],],
aes(x=Day,
y=value0,
color=hgnc_symbol,
linetype = factor(treat,levels=c("Lead30","Control"))))+
scale_color_manual(values = xcolors)+
guides(colour = "none",linetype=guide_legend(" "))
painel1[[1]]
}
library(gridExtra)
library(grid)
lay <- rbind(c(1,2),
c(3,4))
grid<-grid.arrange(painel1[[1]],painel1[[2]],painel1[[3]], painel1[[4]],layout_matrix = lay)
ggsave(filename = paste0("./figures/FigS041a11",
ifelse(normalize,"Norm","Abs"),".pdf"),
plot = grid,
device = "pdf",
#scale = 2.5,
width = 14, height = 8.5, units = "in",
dpi = 300)
#painel 2 ----
genes1<-unique(allTreat$hgnc_symbol[allTreat$type==2])
painel2<-list()
indice<-1
for(indice in 1:length(genes1)){
maxy1<-max(allTreat$value0[allTreat$hgnc_symbol==genes1[indice]])
kst<-allTreat[allTreat$hgnc_symbol==genes1[indice]&
allTreat$Day>0,]
d1<-kst[kst$treat=="Control"&
kst$Day%in%c(3:11),]
d1<-d1$value0[order(d1$Day)]
d2<-kst[kst$treat=="Lead30"&
kst$Day%in%c(3:11),]
d2<-d2$value0[order(d2$Day)]
ks1<-ks.test(d1,d2, alternative = "t")
d1<-kst[kst$treat=="Control"&
kst$Day%in%c(12:26),]
d1<-d1$value0[order(d1$Day)]
d2<-kst[kst$treat=="Lead30"&
kst$Day%in%c(12:26),]
d2<-d2$value0[order(d2$Day)]
ks2<-ks.test(d1,d2, alternative = "t")
timeLabels<-data.frame(label=c("Time-interval 1","Time-interval 2"),
x=c((11-3)/2+3,(26-11)/2+11),
y=rep(maxy1/10,2))
ks1<-p.adjust(c(ks1$p.value,ks2$p.value),
method = "BH")
pval1<-paste0("p-val: ",sprintf("%1.2E",ks1[1]))
pval2<-paste0("p-val: ",sprintf("%1.2E",ks1[2]))
painel2[[indice]]<-ggplot()+theme_bw()+
ggtitle(genes1[indice])+
xlab("Days")+
ylab("Expression")+
geom_rect(aes_(xmin=3,
xmax=11.5,
ymin=0,
ymax=maxy1),
fill="#fbf2cc60")+
geom_rect(aes_(xmin=11.5,
xmax=26,
ymin=0,
ymax=maxy1),
fill="#cce3f260")+
scale_y_continuous(labels = scales::number_format(accuracy = 0.1,
decimal.mark = '.'))+
geom_text(data = timeLabels,
aes(x,y, label=label),
col=alpha(colour = "black",alpha = 0.25))+
geom_text(aes_(x=7,y=(maxy1/4),
label=pval1),
cex=3,
col=ifelse(ks1[1]<=0.01,"blue","red"))+
geom_text(aes_(x=19,y=(maxy1/4),
label=pval2),
cex=3,
col=ifelse(ks1[2]<=0.01,"blue","red"))+
geom_line(data=allTreat[allTreat$hgnc_symbol==genes1[indice],],
aes(x=Day,
y=value0,
color=hgnc_symbol,
linetype = factor(treat,levels=c("Lead30","Control"))))+
# geom_line(data=allTreat[allTreat$treat == "Lead30"&
# allTreat$hgnc_symbol==genes1[indice],],
# aes(x=Day,
# y=value0,
# color=hgnc_symbol),lty=1)+
scale_color_manual(values = xcolors)+
guides(colour = "none",linetype=guide_legend(" "))
}
lay <- rbind(c(1,2),
c(3,4),
c(5,6))
grid<-grid.arrange(painel2[[1]],painel2[[3]],
painel2[[2]], painel2[[4]],
painel2[[5]], painel2[[6]]
,layout_matrix = lay)
ggsave(filename = paste0("./figures/FigS053a11",
ifelse(normalize,"Norm","Abs"),".pdf"),
plot = grid,
device = "pdf",
#scale = 2.5,
width = 14, height = 8.5, units = "in",
dpi = 300)