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EGFRclasses_functions.R
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EGFRclasses_functions.R
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library(reshape2)
library(ggpubr)
library(limma)
library(edgeR)
source("/mnt/ndata/daniele/lung_multiregion/sc/cell-state-inference/utils/dan.functions.R")
DataDir = "/mnt/ndata/daniele/alfredo_egfr/Data/"
CommonDataDir = "/mnt/ed2/daniele/Common_Data/"
ordered_classes = c( "common_ex19del","common_L858R", "common","uncommon","compound","T790M","ex20ins" )
colorz_classes = c("deepskyblue4","deepskyblue3","steelblue4","tomato3","mediumpurple4","darksalmon","orange2")
clco = data.frame(row.names = ordered_classes, classes = ordered_classes, colorz = colorz_classes)
size_labels = 6/.pt
compare_borgeaud_robichaux = function( OutDir ){
load(file = "/mnt/ndata/daniele/alfredo_egfr/Processed/EGFR_classes/ec_exons18_21_oncogenic_alphamissense_filtered.RData" ) # new version, including china
ec = ec[ec$Class %in% c( "common","uncommon" ),]
ec = ec[substr(ec$Mutation,1,2)=="p.",] # exclude c. mutations of TRACERx
ec$Mutation = substr(ec$Mutation,3,nchar(ec$Mutation))
ec = ec[!duplicated(ec$Mutation),]
library(eulerr)
# read robichaux TableS4
er = dan.read(paste0(DataDir,"Robi_mutations_tableS4.txt"))
er = er[!grepl(",",er$mutations),]
er = er[er$four_classes %in% c("classical-like","PACC"),]
# common vs classical/PACC
# er also contains also ex19del. Let's include them directly from ec, and remove the duplicates
er = rbind(er, data.frame( four_classes="classical-like",six_classes="classical-like",mutations=ec[ec$Class=="common","Mutation"],stringsAsFactors=F ))
er = er[!duplicated(er$mutations),]
er = er[!(er$mutations=="Ex19del"),]
classical = sum(er$four_classes=="classical-like")
pacc = sum(er$four_classes=="PACC")
common = sum(ec$Class=="common")
uncommon = sum(ec$Class=="uncommon")
classical_pacc = 0
classical_common = length(intersect(er[er$four_classes=="classical-like","mutations"],ec[ec$Class=="common","Mutation"]))
pacc_common = length(intersect(er[er$four_classes=="PACC","mutations"],ec[ec$Class=="common","Mutation"]))
classical_uncommon = length(intersect(er[er$four_classes=="classical-like","mutations"],ec[ec$Class=="uncommon","Mutation"]))
pacc_uncommon = length(intersect(er[er$four_classes=="PACC","mutations"],ec[ec$Class=="uncommon","Mutation"]))
fit1 <- euler(c("A"=classical-classical_common,"B"=common-classical_common-pacc_common,"C"=pacc-pacc_common,"A&B"=classical_common,"A&C"=0,"B&C"=pacc_common,"A&B&C"=0))
pdf(paste0(OutDir,"borgeaud_vs_robichaux_common.pdf"),2.5,2.5, useDingbats=F,onefile=F,pointsize=6)
plot(fit1,labels = c("Classical-like","common","PACC"),fills = c("pink1","steelblue4","dodgerblue3"),edges = FALSE,fontsize=6,quantities = list(fontsize = 6))
dev.off()
# uncommon vs classical/PACC
fit1 <- euler(c("A"=classical-classical_uncommon,"B"=common-classical_uncommon-pacc_uncommon,"C"=pacc-pacc_uncommon,"A&B"=classical_uncommon,"A&C"=0,"B&C"=pacc_uncommon,"A&B&C"=0))
pdf(paste0(OutDir,"borgeaud_vs_robichaux_uncommon.pdf"),2.5,2.5, useDingbats=F,onefile=F,pointsize=6)
plot(fit1,labels = c("Classical-like","uncommon","PACC"),fills = c("pink1","tomato3","dodgerblue3"),edges = FALSE,fontsize=6,quantities = list(fontsize = 6))
dev.off()
xvenn = list(Robichaux_et_al_2021=pr[(xpval<cutoff_fdr) & (x>0),"gene"],Borgeaud_et_al_2024=pr[(ypval<cutoff_fdr) & (y>0),"gene"])
pdf(paste0(OutDir,"ATG3OE_vs_ATG7OE_up_venn.pdf"),7,7)
ggvenn( xvenn,fill_color = c("darkorange1", "goldenrod"),stroke_size = 0.5,show_elements=F, set_name_size = 4,label_sep='\n', text_size=3)
dev.off()
}
patients_assignment = function( OutDir, retain, using_AlphaMissense = TRUE, split_common = FALSE, compound_to_uncommon = FALSE ){
for (dataset in c( "TCGA","Chen","Genie","Zhang","TRACERx421","Origimed" ) ){
if ( using_AlphaMissense ){
load(file = paste0( DataDir,"using_AlphaMissense/Clin_",dataset,"_complete.RData" ))
} else {
load(file = paste0( DataDir,"Clin_",dataset,"_complete.RData" ))
}
Clin = Clin[Clin$egfr_class_consensus %in% retain,]
if (split_common){
double_commons = Clin[(Clin$egfr_class_consensus=="common") & ((Clin$egfr_class_2=="common") %in% c(T) ),]
double_commons = double_commons[ (double_commons$egfr_mut_1=="p.L858R") | (double_commons$egfr_mut_2=="p.L858R"), ]
Clin = Clin[!(rownames(Clin) %in% rownames(double_commons)),]
Clin[Clin$egfr_class_consensus=="common","egfr_class_consensus"] = ifelse( Clin[Clin$egfr_class_consensus=="common","egfr_mut_1"]=="p.L858R","common_L858R","common_ex19del" )
}
if (compound_to_uncommon){
Clin[Clin$egfr_class_consensus=="compound","egfr_class_consensus"] = "uncommon"
}
save(Clin, file = paste0( OutDir,"Clin_",dataset,".RData" ))
if ( using_AlphaMissense ){
load(file = paste0( DataDir,"using_AlphaMissense/Clin2_",dataset,"_SampleLevel_complete.RData" ))
} else {
load(file = paste0( DataDir,"Clin2_",dataset,"_SampleLevel_complete.RData" ))
}
Clin2 = Clin2[Clin2$egfr_class_consensus %in% retain,]
if (split_common){
double_commons = Clin2[(Clin2$egfr_class_consensus=="common") & ((Clin2$egfr_class_2=="common") %in% c(T) ),]
double_commons = double_commons[ (double_commons$egfr_mut_1=="p.L858R") | (double_commons$egfr_mut_2=="p.L858R"), ]
Clin2 = Clin2[!(rownames(Clin2) %in% rownames(double_commons)),]
Clin2[Clin2$egfr_class_consensus=="common","egfr_class_consensus"] = ifelse( Clin2[Clin2$egfr_class_consensus=="common","egfr_mut_1"]=="p.L858R","common_L858R","common_ex19del" )
}
if (compound_to_uncommon){
Clin2[Clin2$egfr_class_consensus=="compound","egfr_class_consensus"] = "uncommon"
}
save(Clin2, file = paste0( OutDir,"Clin2_",dataset,"_SampleLevel.RData" ))
}
first = TRUE
for (dataset in c( "Genie","TCGA","Chen","TRACERx421","Zhang","Origimed" )){
load(file = paste0( OutDir,"Clin_",dataset,".RData" ))
maxid = max(as.numeric(gsub("egfr_mut_","",colnames(Clin)[substr(colnames(Clin),1,9)=="egfr_mut_"])))
this_cb = dan.df( 0,c("Hugo_Symbol","Sample_ID","Protein_Change","Class" ))
for (id in 1:maxid){
this_cb = rbind(this_cb,data.frame( Hugo_Symbol="EGFR",Sample_ID=Clin$Patient,Protein_Change=gsub("p.","",Clin[,paste0( "egfr_mut_",id )]),Class=Clin[,paste0( "egfr_class_",id )],stringsAsFactors=F ))
}
this_cb = this_cb[!is.na(this_cb$Class),]
this_cb$Dataset = dataset
if (first){
cb = this_cb
first = FALSE
} else {
cb = rbind(cb,this_cb)
}
}
## Lollipop plot of uncommon using patients (to see the most recurrent)
cba = cb[!duplicated(cb$Protein_Change),c( "Protein_Change","Class" )]
rownames(cba) = cba$Protein_Change
tcb = cb[,c("Hugo_Symbol","Sample_ID","Protein_Change")]
prefix = "all"
dan.write(tcb,file=paste0( OutDir,"LollipopPlot_",prefix,"_table.txt" ))
dcat( paste0("Frequency L858R: ",sum(tcb$Protein_Change=="L858R")/nrow(tcb) ))
dcat( paste0("Frequency E746_A750del: ",sum(tcb$Protein_Change=="E746_A750del")/nrow(tcb) ))
tabb = sort(dtable(tcb$Protein_Change),decreasing=T)
tabb = tabb[tabb>=20]
dff = data.frame(Protein_Change = names(tabb), Occurrences = as.numeric(tabb), Class = cba[names(tabb),"Class"],stringsAsFactors=F)
colorz_classes = clco[rownames(clco) %in% (dff$Class),"colorz"]
ordered_classes = clco[rownames(clco) %in% (dff$Class),"classes"]
fileName = paste0( OutDir,"MutOccurrences_",prefix,"_barplot.pdf" )
dan.barplot( fileName, factor(dff$Protein_Change,levels = as.character(dff$Protein_Change)), dff$Occurrences, fill = factor(dff$Class,levels=ordered_classes), xlab = "", ylab = "Occurrences", ylimLeft = NULL, ylimRight = NULL, filllab = "Class", fillColors = colorz_classes, plotTitle = "EGFR mutation occurrences (only >= 20 shown), any class", fileWidth = 4, fileHeight = 2 )
dan.save(dff,fileName)
tcb = cb[cb$Class=="uncommon",c("Hugo_Symbol","Sample_ID","Protein_Change")]
dcat( paste0("Frequency G719A: ",sum(tcb$Protein_Change=="G719A")/nrow(tcb)))
prefix = "uncommon"
dan.write(tcb,file=paste0( OutDir,"LollipopPlot_",prefix,"_table.txt" ))
tabb = sort(dtable(tcb$Protein_Change),decreasing=T)
tabb = tabb[tabb>=20]
dff = data.frame(Protein_Change = names(tabb), Occurrences = as.numeric(tabb), Class = cba[names(tabb),"Class"],stringsAsFactors=F)
colorz_classes = clco[rownames(clco) %in% (dff$Class),"colorz"]
ordered_classes = clco[rownames(clco) %in% (dff$Class),"classes"]
fileName = paste0( OutDir,"MutOccurrences_",prefix,"_barplot.pdf" )
dan.barplot( fileName, factor(dff$Protein_Change,levels = as.character(dff$Protein_Change)), dff$Occurrences, fill = factor(dff$Class,levels=ordered_classes), xlab = "", ylab = "Occurrences", ylimLeft = NULL, ylimRight = NULL, filllab = "Class", fillColors = colorz_classes, plotTitle = "EGFR mutation occurrences (only >= 20 shown), uncommon only", fileWidth = 4, fileHeight = 2 )
dan.save(dff,fileName)
tcb = cb[cb$Class=="common",c("Hugo_Symbol","Sample_ID","Protein_Change")]
prefix = "common"
dan.write(tcb,file=paste0( OutDir,"LollipopPlot_",prefix,"_table.txt" ))
tabb = sort(dtable(tcb$Protein_Change),decreasing=T)
tabb = tabb[tabb>=20]
dff = data.frame(Protein_Change = names(tabb), Occurrences = as.numeric(tabb), Class = cba[names(tabb),"Class"],stringsAsFactors=F)
colorz_classes = clco[rownames(clco) %in% (dff$Class),"colorz"]
ordered_classes = clco[rownames(clco) %in% (dff$Class),"classes"]
fileName = paste0( OutDir,"MutOccurrences_",prefix,"_barplot.pdf" )
dan.barplot( fileName, factor(dff$Protein_Change,levels = as.character(dff$Protein_Change)), dff$Occurrences, fill = factor(dff$Class,levels=ordered_classes), xlab = "", ylab = "Occurrences", ylimLeft = NULL, ylimRight = NULL, filllab = "Class", fillColors = colorz_classes, plotTitle = "EGFR mutation occurrences (only >= 20 shown), common only", fileWidth = 4, fileHeight = 2 )
tcb = cb[,c("Hugo_Symbol","Sample_ID","Protein_Change","Class")]
prefix = "common_aggregated"
tcb[(tcb$Class=="common") & (tcb$Protein_Change!="L858R"),"Protein_Change"] = "ex19del"
tabb = sort(dtable(tcb$Protein_Change),decreasing=T)
tabb = tabb[tabb>=20]
cba["ex19del","Class"] = "common"
dff = data.frame(Protein_Change = names(tabb), Occurrences = as.numeric(tabb), Class = cba[names(tabb),"Class"],stringsAsFactors=F)
colorz_classes = clco[rownames(clco) %in% (dff$Class),"colorz"]
ordered_classes = clco[rownames(clco) %in% (dff$Class),"classes"]
fileName = paste0( OutDir,"MutOccurrences_",prefix,"_barplot.pdf" )
dan.barplot( fileName, factor(dff$Protein_Change,levels = as.character(dff$Protein_Change)), dff$Occurrences, fill = factor(dff$Class,levels=ordered_classes), xlab = "", ylab = "Occurrences", ylimLeft = NULL, ylimRight = NULL, filllab = "Class", fillColors = colorz_classes, plotTitle = "EGFR mutation occurrences (only >= 20 shown)", fileWidth = 4, fileHeight = 2 )
}
compounding_uncommons = function( OutDir ){
load(file = paste0( OutDir,"../Preprocessing/","Clin_Genie.RData" ))
Clin = Clin[Clin$egfr_class_consensus!="common",]
tc = dan.df(0,c( "EGFR_consensus","EGFR_uncommon_single","Dataset" ))
for (rn in rownames(Clin)){
consensus = Clin[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_uncommon_single"))
for (i in 1:5){
if ( (Clin[rn,paste0("egfr_class_",i)]=="uncommon") %in% c(T) ) {
a = Clin[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_uncommon_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = "Genie"
tc = rbind(tc,tcc)
}
load(file = paste0( OutDir,"../Preprocessing/","Clin_TCGA.RData" ))
Clin = Clin[Clin$egfr_class_consensus!="common",]
for (rn in rownames(Clin)){
consensus = Clin[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_uncommon_single"))
for (i in 1:2){
if ( (Clin[rn,paste0("egfr_class_",i)]=="uncommon") %in% c(T) ) {
a = Clin[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_uncommon_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = "TCGA"
tc = rbind(tc,tcc)
}
load(file = paste0( OutDir,"../Preprocessing/","Clin_Chen.RData" ))
Clin = Clin[Clin$egfr_class_consensus!="common",]
for (rn in rownames(Clin)){
consensus = Clin[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_uncommon_single"))
for (i in 1:3){
if ( (Clin[rn,paste0("egfr_class_",i)]=="uncommon") %in% c(T) ) {
a = Clin[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_uncommon_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = "Chen"
tc = rbind(tc,tcc)
}
load(file = paste0( OutDir,"../Preprocessing/","Clin_TRACERx421.RData" ))
Clin = Clin[Clin$egfr_class_consensus!="common",]
for (rn in rownames(Clin)){
consensus = Clin[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_uncommon_single"))
for (i in 1:2){
if ( (Clin[rn,paste0("egfr_class_",i)]=="uncommon") %in% c(T) ) {
a = Clin[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_uncommon_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = "TRACERx421"
tc = rbind(tc,tcc)
}
load(file = paste0( OutDir,"../Preprocessing/","Clin_Zhang.RData" ))
Clin = Clin[Clin$egfr_class_consensus!="common",]
for (rn in rownames(Clin)){
consensus = Clin[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_uncommon_single"))
for (i in 1:2){
if ( (Clin[rn,paste0("egfr_class_",i)]=="uncommon") %in% c(T) ) {
a = Clin[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_uncommon_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = "Zhang"
tc = rbind(tc,tcc)
}
load(file = paste0( OutDir,"../Preprocessing/","Clin_Origimed.RData" ))
Clin = Clin[Clin$egfr_class_consensus!="common",]
for (rn in rownames(Clin)){
consensus = Clin[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_uncommon_single"))
for (i in 1:3){
if ( (Clin[rn,paste0("egfr_class_",i)]=="uncommon") %in% c(T) ) {
a = Clin[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_uncommon_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = "Origimed"
tc = rbind(tc,tcc)
}
save(tc,file=paste0(OutDir,"UncommonVariant_enriched_inCompound_volcanoPlot_tc.RData"))
fileName = paste0(OutDir,"UncommonVariant_enriched_inCompound_volcanoPlot.pdf")
cu = tc
tabb = table(cu[,"EGFR_consensus"],cu$EGFR_uncommon_single)
# tabb = tabb[,colSums(tabb)>=3]
cdf = dan.df(colnames(tabb),c( "Variant","Chisq_residual_compound","Chisq_pval" ))
for (g in colnames(tabb)){
cdf[g,"Variant"] = g
tabb2 = table(cu[,"EGFR_consensus"],cu$EGFR_uncommon_single==g)
if (g %in% c( "p.L861Q","p.S768I" )){ print(tabb2) }
ch = chisq.test(tabb2)
cdf[g,"Chisq_pval"] = ch$p.value
cdf[g,"Chisq_residual_compound"] = ch$residuals["compound","TRUE"]
}
cdf = cdf[order(cdf$Chisq_pval),]
cdf$Chisq_qval = p.adjust(cdf$Chisq_pval,method='BH')
cdf$fill = ifelse( (cdf$Chisq_qval>=0.1) | (abs(cdf$Chisq_residual_compound)<=1),"Not significant",ifelse( cdf$Chisq_residual_compound<0,"Higher in uncommon","Higher in compound"))
cdf$fill = factor(cdf$fill,levels=c( "Higher in uncommon","Higher in compound","Not significant" ))
fillColorz = c( "tomato3","mediumpurple4","gray88" )
cdf$repel_labelz = rownames(cdf)
cdf[cdf$fill=="Not significant","repel_labelz"] = ""
dan.scatterplot( fileName, cdf$Chisq_residual_compound, -log10(cdf$Chisq_pval), fill = cdf$fill, xlab = "Chi-square residual in compound", ylab = "-log10(Chi-square p-value)", filllab = "", fillColors = fillColorz, dotSize = 2, repel_labels=cdf$repel_labelz, coord_fixed = FALSE, fileWidth = 5, fileHeight = 2.5 )
dan.save(cdf,fileName)
}
vaf_class_analysis = function( OutDir ){
dataset = "Genie"
load(file = paste0( OutDir,"../Preprocessing/","Clin_",dataset,".RData" ))
load(file = paste0( OutDir,"Clin2_",dataset,"_SampleLevel.RData" ))
tc = dan.df(0,c( "EGFR_consensus","EGFR_mut_single","EGFR_class_single","Dataset","Sample","Patient" ))
for (rn in rownames(Clin2)){
consensus = Clin2[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_mut_single","EGFR_class_single"))
for (i in 1:5){
if (!is.na(Clin2[rn,paste0("egfr_class_",i)])){
a = Clin2[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ),paste0( "egfr_class_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_mut_single","EGFR_class_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = dataset
tcc$Sample = rn
tcc$Patient = Clin2[rn,"Patient"]
tc = rbind(tc,tcc)
}
load(file = paste0(DataDir,'genie_14.0/maf_Genie_LUAD_all.RData'))
maf_ss = maf_ss[maf_ss$Hugo_Symbol=="EGFR",]
maf_ss$vaf = as.numeric(maf_ss$t_alt_count)/as.numeric(maf_ss$t_depth)
for (rn in rownames(tc)){
s = tc[rn,"Sample"]
mut = tc[rn,"EGFR_mut_single"]
tc[rn,"vaf"] = mean(maf_ss[(maf_ss$Tumor_Sample_Barcode %in% s) & (maf_ss$HGVSp_Short==mut),"vaf"])
}
tcAll = tc
dataset = "TCGA"
load(file = paste0( OutDir,"../Preprocessing/","Clin_",dataset,".RData" ))
load(file = paste0( OutDir,"Clin2_",dataset,"_SampleLevel.RData" ))
tc = dan.df(0,c( "EGFR_consensus","EGFR_mut_single","EGFR_class_single","Dataset","Sample","Patient" ))
for (rn in rownames(Clin2)){
consensus = Clin2[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_mut_single","EGFR_class_single"))
for (i in 1:2){
if (!is.na(Clin2[rn,paste0("egfr_class_",i)])){
a = Clin2[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ),paste0( "egfr_class_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_mut_single","EGFR_class_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = dataset
tcc$Sample = rn
tcc$Patient = Clin2[rn,"Patient"]
tc = rbind(tc,tcc)
}
load(paste0(CommonDataDir,'maf_LUAD_all.RData'))
maf_ss = maf_ss[maf_ss$Hugo_Symbol=="EGFR",]
maf_ss$vaf = as.numeric(maf_ss$t_alt_count)/as.numeric(maf_ss$t_depth)
for (rn in rownames(tc)){
s = tc[rn,"Sample"]
mut = tc[rn,"EGFR_mut_single"]
tc[rn,"vaf"] = mean(maf_ss[(substr(maf_ss$Tumor_Sample_Barcode,1,16) %in% s) & (maf_ss$HGVSp_Short==mut),"vaf"])
}
tcAll = rbind(tcAll,tc)
dataset = "Chen"
load(file = paste0( OutDir,"../Preprocessing/","Clin_",dataset,".RData" ))
load(file = paste0( OutDir,"Clin2_",dataset,"_SampleLevel.RData" ))
tc = dan.df(0,c( "EGFR_consensus","EGFR_mut_single","EGFR_class_single","Dataset","Sample","Patient" ))
for (rn in rownames(Clin2)){
consensus = Clin2[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_mut_single","EGFR_class_single"))
for (i in 1:3){
if (!is.na(Clin2[rn,paste0("egfr_class_",i)])){
a = Clin2[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ),paste0( "egfr_class_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_mut_single","EGFR_class_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = dataset
tcc$Sample = rn
tcc$Patient = Clin2[rn,"Patient"]
tc = rbind(tc,tcc)
}
maf = dan.read(file = paste0("/mnt/ndata/daniele/lung_multiregion/Data/Chen2020/snv_indel.maf"))
maf$Patient = maf$Tumor_Sample_Barcode
maf_ss = maf
maf_ss = maf_ss[maf_ss$Hugo_Symbol=="EGFR",]
maf_ss$vaf = as.numeric(maf_ss$t_alt_count)/(as.numeric(maf_ss$t_ref_count)+as.numeric(maf_ss$t_alt_count))
for (rn in rownames(tc)){
s = tc[rn,"Sample"]
mut = tc[rn,"EGFR_mut_single"]
tc[rn,"vaf"] = mean(maf_ss[(maf_ss$Tumor_Sample_Barcode %in% s) & (maf_ss$HGVSp_Short==mut),"vaf"])
}
tcAll = rbind(tcAll,tc)
dataset = "TRACERx421"
load(file = paste0( OutDir,"../Preprocessing/","Clin_",dataset,".RData" ))
load(file = paste0( OutDir,"Clin2_",dataset,"_SampleLevel.RData" ))
tc = dan.df(0,c( "EGFR_consensus","EGFR_mut_single","EGFR_class_single","Dataset","Sample","Patient" ))
for (rn in rownames(Clin2)){
consensus = Clin2[rn,"egfr_class_consensus"]
tcc = dan.df(0,c("EGFR_consensus","EGFR_mut_single","EGFR_class_single"))
for (i in 1:2){
if (!is.na(Clin2[rn,paste0("egfr_class_",i)])){
a = Clin2[rn,c("egfr_class_consensus",paste0( "egfr_mut_",i ),paste0( "egfr_class_",i ))]
colnames(a) = c("EGFR_consensus","EGFR_mut_single","EGFR_class_single")
tcc = rbind(tcc,a)
}
}
tcc$Dataset = dataset
tcc$Sample = rn
tcc$Patient = Clin2[rn,"Patient"]
tc = rbind(tc,tcc)
}
library(fst)
maf = read_fst(paste0(DataDir,"TRACERx_421/figurecode/data/20221109_TRACERx421_mutation_table.fst"))
mafr = read_fst(paste0(DataDir,"TRACERx_421/figurecode/data/20221123_TRACERx421_mutation_table_region.fst"))
maf_ss = maf[maf$tumour_id %in% Clin2$Sample,]
maf_ss$Sample = maf_ss$tumour_id
maf_ss$Patient = maf_ss$patient_id
maf_ss = maf_ss[maf_ss$Hugo_Symbol=="EGFR",]
for (rn in rownames(maf_ss)){
thiz = mafr[mafr$mutation_id==maf_ss[rn,"mutation_id"],]
maf_ss[rn,"vaf"] = mean(as.numeric(thiz$var_count)/(as.numeric(thiz$depth)))
}
maf_ss$HGVSp_Short = maf_ss$AAChange
maf_ss[is.na(maf_ss$HGVSp_Short),"HGVSp_Short"] = maf_ss[is.na(maf_ss$HGVSp_Short),"NucleotideChange"]
for (rn in rownames(tc)){
s = tc[rn,"Sample"]
mut = tc[rn,"EGFR_mut_single"]
tc[rn,"vaf"] = mean(maf_ss[(maf_ss$patient_id %in% s) & (maf_ss$HGVSp_Short==mut),"vaf"])
}
tcAll = rbind(tcAll,tc)
## no vaf info for Origimed, Zhang
tcAll_save = tcAll
tcAll = tcAll_save[(((tcAll_save$vaf<=1) & (tcAll_save$vaf>=0))) %in% c(T),]
save(tcAll,file=paste0(OutDir,"tcAll_vafs.RData"))
x = factor(tcAll[,"Dataset"])
y = tcAll$vaf
dan.densityPlot( paste0(OutDir,"vaf_across_Datasets.pdf"), y, x, groupinglab = "", xlab = paste0("Variant allele frequency"), ylab = "Density", show_medians = F, plotTitle = "",xlimLeft = 0, xlimRight = 1, groupingColors = c("red","blue","green","yellow" ), fileWidth = 3.5, fileHeight = 2 )
colorz_classes = clco[rownames(clco) %in% (tc$EGFR_consensus),"colorz"]
ordered_classes = clco[rownames(clco) %in% (tc$EGFR_consensus),"classes"]
# consensus class
x = factor(tcAll[,"EGFR_consensus"],levels = ordered_classes)
plotTitle = paste0( "Kruskal-Wallis test, p-value = ", signif(kruskal.test(y~x)$p.value,2))
dan.densityPlot( paste0(OutDir,"vaf_across_EGFR_consensus.pdf"), y, x, groupinglab = "", xlab = paste0("Variant allele frequency"), ylab = "Density", show_medians = F, plotTitle = plotTitle,xlimLeft = 0, xlimRight = 1, groupingColors = colorz_classes, fileWidth = 3.5, fileHeight = 2 )
# single mutations
x = factor(tcAll[,"EGFR_class_single"],levels = c( "common","uncommon" ))
plotTitle = paste0( "Wilcoxon test, p-value = ", signif(wilcox.test(y~x)$p.value,2))
these_colorz = colorz_classes[ordered_classes %in% levels(x)]
dan.densityPlot( paste0(OutDir,"vaf_across_EGFR_single_mutations.pdf"), y, x, groupinglab = "", xlab = paste0("Variant allele frequency"), ylab = "Density", show_medians = F, plotTitle = plotTitle,xlimLeft = 0, xlimRight = 1, groupingColors = these_colorz, fileWidth = 3.5, fileHeight = 2 )
# single uncommon - consensus uncommon vs compound
thiz = tcAll[tcAll$EGFR_class_single=="uncommon",]
x = factor(thiz[,"EGFR_consensus"],levels = c( "uncommon","compound" ))
y = thiz$vaf
plotTitle = paste0( "Wilcoxon test, p-value = ", signif(wilcox.test(y~x)$p.value,2))
these_colorz = colorz_classes[ordered_classes %in% levels(x)]
dan.densityPlot( paste0(OutDir,"vaf_across_EGFR_single_uncommon_acrossConsensus.pdf"), y, x, groupinglab = "", xlab = paste0("Variant allele frequency"), ylab = "Density", show_medians = F, plotTitle = plotTitle,xlimLeft = 0, xlimRight = 1, groupingColors = these_colorz, fileWidth = 3.5, fileHeight = 2 )
# single common - consensus common vs compound
thiz = tcAll[tcAll$EGFR_class_single=="common",]
x = factor(thiz[,"EGFR_consensus"],levels = c( "common","compound" ))
y = thiz$vaf
plotTitle = paste0( "Wilcoxon test, p-value = ", signif(wilcox.test(y~x)$p.value,2))
these_colorz = colorz_classes[ordered_classes %in% levels(x)]
dan.densityPlot( paste0(OutDir,"vaf_across_EGFR_single_common_acrossConsensus.pdf"), y, x, groupinglab = "", xlab = paste0("Variant allele frequency"), ylab = "Density", show_medians = F, plotTitle = plotTitle,xlimLeft = 0, xlimRight = 1, groupingColors = these_colorz, fileWidth = 3.5, fileHeight = 2 )
}
fisher_tests = function( clin, vars ){
for (var in vars){
dcat(var)
tc = clin[!is.na(clin[,var]),]
ft = fisher.test(table(tc[,var],tc$egfr_class_consensus),simulate.p.value = TRUE)
print(ft)
}
}
clinical_associations = function( OutDir ){
load(file = paste0( OutDir,"../Preprocessing/","Clin_Genie.RData" ))
Cling = Clin
load(file = paste0( OutDir,"../Preprocessing/","Clin2_Genie_SampleLevel.RData" ))
Clin2g = Clin2
load(file = paste0( OutDir,"../Preprocessing/","Clin_TCGA.RData" ))
Clint = Clin
load(file = paste0( OutDir,"../Preprocessing/","Clin2_TCGA_SampleLevel.RData" ))
Clin2t = Clin2
load(file = paste0( OutDir,"../Preprocessing/","Clin_Chen.RData" ))
Clinc = Clin
load(file = paste0( OutDir,"../Preprocessing/","Clin2_Chen_SampleLevel.RData" ))
Clin2c = Clin2
load(file = paste0( OutDir,"../Preprocessing/","Clin_TRACERx421.RData" ))
Clinx = Clin
load(file = paste0( OutDir,"../Preprocessing/","Clin_Zhang.RData" ))
Clinz = Clin
load(file = paste0( OutDir,"../Preprocessing/","Clin_Origimed.RData" ))
Clino = Clin
colorz_classes = clco[rownames(clco) %in% (Clin2g$egfr_class_consensus),"colorz"]
ordered_classes = clco[rownames(clco) %in% (Clin2g$egfr_class_consensus),"classes"]
## Let's do it at patient level. Survival excluded, for now
rm(Clin)
rm(Clin2)
common_vars = c( "Stage","Sex","Race","Ethnicity","Smoking","Pattern","Age" )
Cling$Age = as.numeric(Cling$Age)
Clin = Cling
save(Clin, file = paste0( OutDir,"../Preprocessing/","Clin_Genie.RData" ))
Cling = Cling[,c("Patient","egfr_class_consensus", common_vars )]
Clint$Stage = NA
Clint[ Clint$pathologic_stage %in% c( "stage i","stage ia","stage ib" ) ,"Stage" ] = "I"
Clint[ Clint$pathologic_stage %in% c( "stage iia","stage iib" ) ,"Stage" ] = "II"
Clint[ Clint$pathologic_stage %in% c( "stage iiia","stage iiib" ) ,"Stage" ] = "III"
Clint[ Clint$pathologic_stage %in% c( "stage iv" ) ,"Stage" ] = "IV"
Clint$Sex = Clint$gender
Clint$Race = Clint$race
Clint[(Clint$Race=="black or african american") %in% c(T),"Race"] = "black"
Clint$Ethnicity = tolower(Clint$ethnicity)
Clint[(Clint$Ethnicity=="not hispanic or latino") %in% c(T),"Ethnicity"] = "non-hispanic"
Clint$Smoking = NA
Clint[(Clint$tobacco_smoking_history=="1") %in% c(T),"Smoking"] = "never smoker"
Clint[(Clint$tobacco_smoking_history %in% c("2","3","4") ) %in% c(T),"Smoking"] = "ever smoker"
Clint$Age = as.numeric(Clint$age_at_initial_pathologic_diagnosis)
Clin = Clint
save(Clin, file = paste0( OutDir,"../Preprocessing/","Clin_TCGA.RData" ))
Clint = Clint[,c("Patient","egfr_class_consensus", common_vars )]
Clinc$Sex = tolower(Clinc$Gender)
Clinc$Race = "asian"
Clinc$Ethnicity = "non-hispanic"
Clinc$Smoking = ifelse( (Clinc$Smoker=="Yes") %in% c(T),"ever smoker", ifelse( (Clinc$Smoker=="No") %in% c(T),"never smoker",NA ))
Clinc$Age = as.numeric(Clinc$Age)
Clin = Clinc
save(Clin, file = paste0( OutDir,"../Preprocessing/","Clin_Chen.RData" ))
Clinc = Clinc[,c("Patient","egfr_class_consensus", common_vars )]
Clinx = Clinx[,c("Patient","egfr_class_consensus", common_vars )]
Clinz = Clinz[,c("Patient","egfr_class_consensus", common_vars )]
Clino = Clino[,c("Patient","egfr_class_consensus", common_vars )]
## Tests
dcat( "Genie" )
fisher_tests( Cling, common_vars )
dcat( "TCGA" )
fisher_tests( Clint, c( "Stage","Sex","Race","Smoking","Pattern" ) )
dcat( "Chen" )
fisher_tests( Clinc, c( "Stage","Sex","Smoking","Pattern" ) )
dcat( "TRACERx421" )
fisher_tests( Clinx, c( "Stage","Sex","Race","Smoking","Pattern" ) )
dcat( "Origimed" )
fisher_tests( Clino, c( "Stage","Sex","Smoking" ) )
Clinall = rbind(rbind(rbind(Cling,Clint),rbind(Clinc,Clinx) ),Clinz)
Clinall = rbind(Clinall,Clino)
dcat( "All datasets" )
fisher_tests( Clinall, c( "Stage","Sex","Race","Smoking","Pattern" ) )
# Sex: negative association
tc = Clinall[!is.na(Clinall[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2,size = 6/.pt) + geom_text(aes(label=round(Percentage,1)), size = 6/.pt, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Sex_onlyAll_barplot.pdf"),2.8,2.8,onefile=FALSE)
pa = pa + theme(text = element_text(size=6), legend.text=element_text(size=6), legend.title = element_text(size = 6), plot.title = element_text(size = 6), axis.title = element_text(size = 6), axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6), legend.key.size = unit(10, 'pt'))
print(pa)
dev.off()
tc = Clinall[!is.na(Clinall[,"Stage"]),]
tabb = table(tc[,"Stage"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Stage","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Stage=="I") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Stage=="I"))
mt[(mt$Stage=="II") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Stage=="II"))
mt[(mt$Stage=="III") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Stage=="III"))
mt[(mt$Stage=="IV") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Stage=="IV"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Stage, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("Tumor stage") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2,size = 6/.pt) + geom_text(aes(label=round(Percentage,1)), size = 6/.pt, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Stage_onlyAll_barplot.pdf"),3,2.5,onefile=FALSE)
pa = pa + theme(text = element_text(size=6), legend.text=element_text(size=6), legend.title = element_text(size = 6), plot.title = element_text(size = 6), axis.title = element_text(size = 6), axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6), legend.key.size = unit(10, 'pt'))
print(pa)
dev.off()
tc = Clinall[!is.na(Clinall[,"Race"]),]
# tc = tc[!( tc$Race %in% c( "native american","pacific islander" ) ),]
tc$Race2 = "non-asian"
tc$Race2[(tc$Race=="asian") %in% c(T)] = "asian"
tc$Race = tc$Race2
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","non-asian" ) )
mt$TotalPatients = NA
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt[(mt$Race=="non-asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="non-asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2,size = 6/.pt) + geom_text(aes(label=round(Percentage,1)), size = 6/.pt, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Race_onlyAll_barplot.pdf"),2.8,2.8,onefile=FALSE)
pa = pa + theme(text = element_text(size=6), legend.text=element_text(size=6), legend.title = element_text(size = 6), plot.title = element_text(size = 6), axis.title = element_text(size = 6), axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6), legend.key.size = unit(10, 'pt'))
print(pa)
dev.off()
tc = Clinall[!is.na(Clinall[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2,size = 6/.pt) + geom_text(aes(label=round(Percentage,1)), size = 6/.pt, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Smoking_onlyAll_barplot.pdf"),2.8,2.8,onefile=FALSE)
pa = pa + theme(text = element_text(size=6), legend.text=element_text(size=6), legend.title = element_text(size = 6), plot.title = element_text(size = 6), axis.title = element_text(size = 6), axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6), legend.key.size = unit(10, 'pt'))
print(pa)
dev.off()
tc = Clinall[!is.na(Clinall[,"Age"]),]
tc$Age = as.numeric(tc$Age)
fileName = paste0(OutDir,"EGFRclasses_Age_onlyAll_boxplot.pdf")
tc$egfr_class_consensus = factor(tc$egfr_class_consensus,levels=ordered_classes)
jitterColors = dan.expand_colors( tc$egfr_class_consensus, ordered_classes, colorz_classes )
dan.boxplots(fileName,x = tc$egfr_class_consensus, y=tc$Age, xlab="", ylab="Age (years)", labelycoo=max(tc$Age), xColors=colorz_classes, jitterColors=jitterColors,jitterDotSize=1, fileWidth = 2.5, fileHeight = 2.5)
kruskal.test(Age~egfr_class_consensus,data=Clinall)
kruskal.test(Age~egfr_class_consensus,data=Cling)
kruskal.test(Age~egfr_class_consensus,data=Clinc)
kruskal.test(Age~egfr_class_consensus,data=Clint)
kruskal.test(Age~egfr_class_consensus,data=Clinx)
kruskal.test(Age~egfr_class_consensus,data=Clino)
# Sex
tc = Cling[!is.na(Cling[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pg = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Genie\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clint[!is.na(Clint[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pt = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("TCGA\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinc[!is.na(Clinc[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pc = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("ChenEAS\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinx[!is.na(Clinx[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
px = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("TRACERx421\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinz[!is.na(Clinz[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pz = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Zhang\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clino[!is.na(Clino[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
po = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Origimed\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinall[!is.na(Clinall[,"Sex"]),]
tabb = table(tc[,"Sex"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Sex=="female") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="female"))
mt[(mt$Sex=="male") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Sex=="male"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Sex_All_barplot.pdf"),21,5,onefile=FALSE)
plot = ggarrange(pg, pt, pc, px,pz,po, pa, common.legend=TRUE,ncol = 7, nrow = 1)
print(plot)
dev.off()
# Race
tc = Cling[!is.na(Cling[,"Race"]),]
tc = tc[!( tc$Race %in% c( "native american","pacific islander" ) ),]
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","black","white" ) )
mt$TotalPatients = NA
mt[(mt$Race=="white") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="white"))
mt[(mt$Race=="black") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="black"))
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pg = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Genie\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clint[!is.na(Clint[,"Race"]),]
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","black","white" ) )
mt$TotalPatients = NA
mt[(mt$Race=="white") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="white"))
mt[(mt$Race=="black") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="black"))
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pt = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("TCGA\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinc[!is.na(Clinc[,"Race"]),]
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","black","white" ) )
mt$TotalPatients = NA
mt[(mt$Race=="white") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="white"))
mt[(mt$Race=="black") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="black"))
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pc = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("ChenEAS\n" )) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinx[!is.na(Clinx[,"Race"]),]
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","black","white" ) )
mt$TotalPatients = NA
mt[(mt$Race=="white") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="white"))
mt[(mt$Race=="black") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="black"))
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
px = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("TRACERx421\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clino[!is.na(Clino[,"Race"]),]
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","black","white" ) )
mt$TotalPatients = NA
mt[(mt$Race=="white") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="white"))
mt[(mt$Race=="black") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="black"))
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
po = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Origimed\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinall[!is.na(Clinall[,"Race"]),]
tc = tc[!( tc$Race %in% c( "native american","pacific islander" ) ),]
tabb = table(tc[,"Race"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Race","EGFR_class","Percentage" )
mt$Race = factor( mt$Race, levels = c( "asian","black","white" ) )
mt$TotalPatients = NA
mt[(mt$Race=="white") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="white"))
mt[(mt$Race=="black") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="black"))
mt[(mt$Race=="asian") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Race=="asian"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Race, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Race_All_barplot.pdf"),18,5,onefile=FALSE)
plot=ggarrange(pg, pt, pc, px, po, pa, common.legend=TRUE,ncol = 6, nrow = 1)
print(plot)
dev.off()
# Smoking
tc = Cling[!is.na(Cling[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pg = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Genie\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clint[!is.na(Clint[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pt = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("TCGA\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinc[!is.na(Clinc[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pc = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("ChenEAS\n","Chi-square test, p = ",signif(ch,2))) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinx[!is.na(Clinx[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
px = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("TRACERx421\n","Chi-square test, p = ",signif(ch,2))) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clino[!is.na(Clino[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
po = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Origimed\n","Chi-square test, p = ",signif(ch,2))) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinz[!is.na(Clinz[,"Smoking"]),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pz = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Zhang\n")) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
tc = Clinall[!is.na(Clinall[,"Smoking"]),]
tc = tc[!( tc$Smoking %in% c( "native american","pacific islander" ) ),]
tabb = table(tc[,"Smoking"],tc$egfr_class_consensus)
ch = chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Smoking","EGFR_class","Percentage" )
mt$TotalPatients = NA
mt[(mt$Smoking=="ever smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="ever smoker"))
mt[(mt$Smoking=="never smoker") & (mt$EGFR_class=="common"),"TotalPatients"] = paste0( "N = ",sum(tc$Smoking=="never smoker"))
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pa = ggplot(data=mt, aes(x=Smoking, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("All datasets\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) +# scale_x_discrete(labels=rownames(ldf)) +
geom_text(aes(label=TotalPatients,y=100),vjust=-0.2) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
pdf(paste0(OutDir,"EGFRclasses_Smoking_All_barplot.pdf"),21,5,onefile=FALSE)
plot=ggarrange(pg, pt, pc, px,pz,po, pa, common.legend=TRUE,ncol = 7, nrow = 1)
print(plot)
dev.off()
## Are variables independent or not? Using Clinall only. Not really independent..
tc = Clinall
tc = tc[!( tc$Race %in% c( "native american","pacific islander" ) ),]
tabb = table(tc$Sex,tc$Race)
chisq.test(tabb)$p.value
t(apply(tabb,1, function(x) x/sum(x)))*100
tc = Clinall
tc = tc[!( tc$Race %in% c( "native american","pacific islander" ) ),]
tabb = table(tc$Smoking,tc$Race)
chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
tabb
tc = Clinall
tabb = table(tc$Smoking,tc$Sex)
chisq.test(tabb)$p.value
tabb = t(apply(tabb,1, function(x) x/sum(x)))*100
tabb
# Let's try to account for variables
tc = Clinall
# tc = tc[!( tc$Race %in% c( "native american","pacific islander" ) ),]
tc$Race2 = "non-asian"
tc$Race2[(tc$Race=="asian") %in% c(T)] = "asian"
tc$Race2[is.na(tc$Race)] = NA
dtable(tc$Race2)
tabb = table(tc$Smoking,tc$egfr_class_consensus,tc$Sex,tc$Race2)
tabb
# test if sex difference depends on race difference
tc1 = tc[(tc$Race2=="asian") %in% c(T),]
tabb1= table(tc1[,"Sex"],tc1$egfr_class_consensus)
ch = chisq.test(tabb1)$p.value
tabb = t(apply(tabb1,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pAsian = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Asians (N = ",sum(tabb1),")\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
pAsian = pAsian + theme(text = element_text(size=6), legend.text=element_text(size=6), legend.title = element_text(size = 6), plot.title = element_text(size = 6), axis.title = element_text(size = 6), axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6), legend.key.size = unit(10, 'pt'))
tc1 = tc[(tc$Race2=="non-asian") %in% c(T),]
tabb1= table(tc1[,"Sex"],tc1$egfr_class_consensus)
ch = chisq.test(tabb1)$p.value
tabb = t(apply(tabb1,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)
pNonAsian = ggplot(data=mt, aes(x=Sex, y=Percentage, fill=EGFR_class)) +
geom_bar(stat="identity", colour="black", linewidth = 0.1) + ylab("Percentage of patients") + xlab("") + ggtitle(paste0("Non-asians (N = ",sum(tabb1),")\n","Chi-square test, p = ",signif(ch,2)) ) + scale_fill_manual(name = "EGFR mutation class",values=colorz_classes) + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1,size=12)) + geom_text(aes(label=round(Percentage,1)), size = size_labels, position = position_stack(vjust = 0.5))
pNonAsian = pNonAsian + theme(text = element_text(size=6), legend.text=element_text(size=6), legend.title = element_text(size = 6), plot.title = element_text(size = 6), axis.title = element_text(size = 6), axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6), legend.key.size = unit(10, 'pt'))
pdf(paste0(OutDir,"EGFRclasses_Sex_SplittedByRace_All_barplot.pdf"),4,3,onefile=FALSE)
plot=ggarrange(pAsian, pNonAsian, common.legend=TRUE,ncol = 2, nrow = 1)
print(plot)
dev.off()
# test if sex difference depends on smoking difference
tc1 = tc[(tc$Smoking=="ever smoker") %in% c(T),]
tabb1= table(tc1[,"Sex"],tc1$egfr_class_consensus)
ch = chisq.test(tabb1)$p.value
tabb = t(apply(tabb1,1, function(x) x/sum(x)))*100
mt = melt(tabb)
colnames(mt) = c( "Sex","EGFR_class","Percentage" )
mt$EGFR_class = factor(mt$EGFR_class, levels=ordered_classes)