inst/Tcell_dysfunction_analysis/analysis.R
In Bozorgui/imogene: : Immuno-oncology gene expression associations
#This code reproduces assessment of combinatorial interactions for T-cell dysfunction signature genes in Uterine Corpus Endometrial Carcinoma (UCEC).
# BAsed on ImogiMap package. Developed by Behnaz Bozorgui for the Korkut lab at MDA.
##Install from github-----------------------------
library(remotes)
install_github('korkutlab/imogimap')
library(imogimap)
#Input--------------------------------------------
#Set directory
current_dir <- dirname(rstudioapi::getSourceEditorContext()$path)
setwd(current_dir)
#UCEC T cell dysfunction genes
df_TCDF_genes<- read.csv("TCell_dys_genes_ucec.csv",header = T)
#Calculate and evaluate synergy scores------------
#Specificity and robustness evaluation of scores may take some time.
#To skip these evaluations set specificity and sensitivity(Robustness) to FALSE.
my_scores <- im_syng_tcga( onco_gene=df_TCDF_genes$Gene,
icp_gene = icp_gene_list,
cohort="ucec",
select_iap = "IFNGscore",
specificity = F,
sensitivity = F,
N_iteration_specificity = 1000,
N_iteration_sensitivity = 1000)
my_scores <- my_scores[-which(is.na(my_scores$Synergy_score)),]
#Compare-----------------------------------------
#To compare your results with ours
df_scores <- read.csv("TCell_dys_scores.csv")
df_scores <- df_scores[-which(is.na(df_scores$Synergy_score)),]
library(ggplot2)
library(circlize)
#Plot2A---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
while (!is.null(dev.list())) dev.off()
ggplot(df,aes(Synergy_score,r))+
geom_abline(slope =0,intercept = 1,color="red",size=2)+
geom_point(cex=1.5)+ theme_bw()+
xlim(c(-0.8,0.8))+
#ylim(c(-3,3))+
labs(title= "", x= "Synergy scores", y= "-log(R) Robustness",tag="1")+
guides(x.sec = "axis", y.sec = "axis") +
theme( axis.text.x = element_text(size=30,angle = 90),
axis.text.y = element_text(size=30,angle = 90),
axis.title = element_text(size=30),
plot.margin=unit(c(1,1,1.5,1.2),"cm"),
plot.tag.position = c(.05, 1.15),
plot.tag = element_text(size=60,colour="red",hjust = -0.3,vjust = 9),
axis.line.x.top = element_line( size = 1),
axis.line.x.bottom = element_line( size = 1),
axis.line.y.left = element_line( size = 1),
axis.line.y.right = element_line( size = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#Plot2B---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
df <- df[df$r>1,]
n <- nrow(df)
df$Q <- p.adjust(df$wilcox_pvalue,method="BH",n =n)
while (!is.null(dev.list())) dev.off()
par(mar = c(5,7,10,20))
ggplot(df,aes(Synergy_score,-log((Q))))+
geom_abline(slope =0,intercept = -log(0.1),color="red",size=2)+
geom_point(size=1.5)+ theme_bw()+xlim(c(-0.8,0.8))+
labs(title= "", x= "Synergy scores", y= " -log(Q) Significance\n",tag = "-log(0.1)")+
guides(x.sec = "axis", y.sec = "axis") +
theme( axis.text.x = element_text(size=30,angle = 90),
axis.text.y = element_text(size=30,angle = 90),
axis.title = element_text(size=30),
axis.line.x.top = element_line( size = 1),
axis.line.x.bottom = element_line( size = 1),
axis.line.y.left = element_line( size = 1),
axis.line.y.right = element_line( size = 1),
plot.margin=unit(c(1,1,1.5,1.2),"cm"),
plot.tag.position = c(.14, .4),
plot.tag = element_text(size=30,colour="red",hjust = -0.1,vjust = 1.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#Plot2C---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
df <- df[df$r>1,]
n <- nrow(df)
df$Q <- p.adjust(df$wilcox_pvalue,method="BH",n =n)
df <- df[df$Q<0.1,]
n<- nrow(df)
df$Q <- p.adjust(df$specificity_pvalue,method="BH",n =n)
while (!is.null(dev.list())) dev.off()
par(mar = c(5,7,10,20))
ggplot(df,aes(Synergy_score,-log((Q))))+
geom_abline(slope =0,intercept = -log(0.1),color="red",size=2)+
geom_point(size=2)+ theme_bw()+xlim(c(-0.8,0.8))+
guides(x.sec = "axis", y.sec = "axis") +
labs(title= "", x= "Synergy scores", y= "-log(Q) Specificity",tag = "-log(0.1)")+
theme( axis.text.x = element_text(size=30,angle = 90),
axis.text.y = element_text(size=30,angle = 90),
axis.title = element_text(size=30),
axis.line.x.top = element_line( size = 1),
axis.line.x.bottom = element_line( size = 1),
axis.line.y.left = element_line( size = 1),
axis.line.y.right = element_line( size = 1),
plot.margin=unit(c(1,1,1.5,1.2),"cm"),
plot.tag.position = c(.1, .65),
plot.tag = element_text(size=30,colour="red",hjust = -0.1,vjust = 1.),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#plot2D---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
df <- df[df$r>1,]
n <- nrow(df)
df$Q <- p.adjust(df$wilcox_pvalue,method="BH",n =n)
df <- df[df$Q<0.1,]
n<- nrow(df)
df$Q <- p.adjust(df$specificity_pvalue,method="BH",n =n)
df <- df[df$Q<0.1,]
#change seed to explore different layouts
while (!is.null(dev.list())) dev.off()
im_netplot(df =df, Immune_phenotype ="IFNGscore",cutoff = 0.,seed=3)
#Plot2E---------------------------------------------
while (!is.null(dev.list())) dev.off()
im_boxplot_tcga(onco_gene = "SERPINB9",
icp_gene = "CTLA4",
cohort = "ucec",
Immune_Feature ="IFNGscore",
logtrans = F)
dev.off()
#Plot2F---------------------------------------------
while (!is.null(dev.list())) dev.off()
im_boxplot_tcga(onco_gene = "CD70",
icp_gene = "CD86",
cohort = "ucec",
Immune_Feature ="IFNGscore",
logtrans = F)
#Plot2G---------------------------------------------
library(survival)
library(survminer)
#Read TCGA UCEC survival values
#Survival values are taken from Liu J., et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173,400 (2018).
clin <- read.csv("TCGA_PFIsurvival_UCEC.csv",header = T)
#Read TCGA UCEC expression
df <-curatedTCGAData::curatedTCGAData( diseaseCode = "ucec",
assays = c("RNASeq2GeneNorm"),
dry.run = F)@ExperimentList@listData[[1]]
data_expression <- df@assays$data@listData[[1]]
colnames(data_expression)<- substr(colnames(data_expression), 1, 15)
#Select genes
df_selected <- t(data_expression[rownames(data_expression ) %in% c("CD86","CD70"),])
#Log2p1 transform
df_selected <- scale(log2(df_selected+1),center = T,scale = T)
#Take mean of multiple samples
df_selected <- as.data.frame(df_selected)
df_selected$bcr_patient_barcode <- substr(rownames(df_selected), 1, 12)
df_selected <- aggregate(list(df_selected$CD70,df_selected$CD86),
list(df_selected$bcr_patient_barcode), FUN=mean)
rownames(df_selected)<- df_selected$Group.1
df_selected$Group.1<-NULL
colnames(df_selected)<- c("CD70","CD86")
df_selected <- as.matrix(df_selected)
#Get quantile ranks for each sample
df_select_qr <- get_quantile_rank(df_selected)
df_select_qr$bcr_patient_barcode <- df_select_qr$Tumor_Sample_ID
#Select samples with expression values in Q1 or Q3 quantile.
df_select_qr <- cbind(df_select_qr,as.integer((df_select_qr[,2]*2+df_select_qr[,3])/3))
df_select_qr <- df_select_qr[df_select_qr[,5]%in% c(1,4),]
colnames(df_select_qr)[5]<-"status"
#Merge data
df_surv <- merge(df_select_qr ,clin,by="bcr_patient_barcode")
df_surv$pcevent <- df_surv$PFI
df_surv$pctime <- df_surv$PFI.time
df_surv <- df_surv[!is.na(df_surv$pctime),]
df_surv <- df_surv[,c("Tumor_Sample_ID","pcevent","pctime","status")]
#Survival analysis
cox1 <- coxph( Surv(pctime,pcevent) ~ status , data = df_surv )
hr <- exp(cox1$coefficients)
fit1 <- survfit( Surv(pctime,pcevent) ~ status , data = df_surv ,type="kaplan-meier")
pval <- surv_pvalue(
fit1,
data = df_surv,
method = "Log-rank"
)$pval
#plot
while (!is.null(dev.list())) dev.off()
ggsurv <- ggsurvplot(fit1, data = df_surv, risk.table=F, size = 2,
palette = c("blue","red"),
xlab="Time (Days)", legend.title="",
legend.labs = c("Low CD86 Low CD70","High CD86 High CD70"),
legend=c(0.7,0.4),font.legend=30,
#pval=T,pval.method=T,
font.x=30,font.y=30,font.tickslab=30)
ggsurv$plot <- ggsurv$plot +
ggplot2::annotate(
"text",
x = 0, y = 0,
vjust = 0.1, hjust = 0,
label = paste0("Log-Rank Pvalue", pval ,"\nHazard Ratio ,",hr),
size = 10
)
ggsurv
#Supplementary figure----------------------------------------
df_all<-list()
#Read TCGA mRNA data for all disease cohorts
for(cohortID in 1:length(TCGA_disease_list)){
disease <- TCGA_disease_list[cohortID]
message("\nReading TCGA ",toupper(disease)," data\n")
df <-curatedTCGAData::curatedTCGAData( diseaseCode =disease,
assays = c("RNASeq2GeneNorm"), dry.run = F)@ExperimentList@listData[[1]]
df <- df@assays$data@listData[[1]]
colnames(df)<- substr(colnames(df), 1, 15)
df_all[[cohortID]] <- df
}
df <- lapply(df_all, as.data.frame)
df <- lapply(df, function(x){x$gene <- rownames(x); x})
df <-Reduce(merge,df)
rownames(df)<-df$gene
df$gene<-NULL
#Calculate IAP values
df1 <- get_emt_score(df)
df2 <- get_angio_score(df)
df3 <- get_ifng_score(df)
df4 <-TCGA_Leukocyte_fraction
df5 <-TCGA_TMB[,c("Tumor_Sample_ID","TMB_Non.silent_per_Mb")]
df6 <- TCGA_TMB[,c("Tumor_Sample_ID","TMB_Silent_per_Mb")]
data_feature <- get_features(df)
d1<- merge(df1, data_feature[,c("Tumor_Sample_ID" ,"EMTscore")],by= "Tumor_Sample_ID")
d2<- merge(df2, data_feature[,c("Tumor_Sample_ID" ,"AGscore")],by= "Tumor_Sample_ID")
d3<- merge(df3, data_feature[,c("Tumor_Sample_ID" ,"IFNGscore")],by= "Tumor_Sample_ID")
d4<- merge(df4, data_feature[,c("Tumor_Sample_ID" ,"Leukocyte_fraction")],by= "Tumor_Sample_ID")
d5<- merge(df5, data_feature[,c("Tumor_Sample_ID" ,"TMB_Non.silent_per_Mb")],by= "Tumor_Sample_ID")
d6<- merge(df6, data_feature[,c("Tumor_Sample_ID" ,"TMB_Silent_per_Mb")],by= "Tumor_Sample_ID")
#plot
while (!is.null(dev.list())) dev.off()
plot(d1$EMTscore.x,d1$EMTscore.y,
xlab="EMT score",ylab="Transformed\n EMT score")
plot(d2$AGscore.x,d2$AGscore.y,
xlab="Vascularization",ylab="Transformed\n Vascularization")
plot(d3$IFNGscore.x,d3$IFNGscore.y,
xlab="IFNG score",ylab="Transformed\n IFNG score")
plot(d4$Leukocyte_fraction.x,d4$Leukocyte_fraction.y,
xlab="Leukocyte fraction",ylab="Transformed\n Leukocyte fraction")
plot(d5$TMB_Non.silent_per_Mb.x,d5$TMB_Non.silent_per_Mb.y,
xlab="Non.silent TMB",ylab="Transformed\n Non.silent TMB")
plot(d6$TMB_Silent_per_Mb.x,d6$TMB_Silent_per_Mb.y,
xlab="Silent TMB",ylab="Transformed\n Silent TMB")
hist(df1$EMTscore,breaks=20,xlab="EMT score",main = "")
hist(df2$AGscore,breaks=20,xlab="Vascularization",main = "")
hist(df3$IFNGscore,breaks=20,xlab="IFNG score",main = "")
hist(df4$Leukocyte_fraction,breaks=20,xlab="Leukocyte fraction",main = "")
hist(df5$TMB_Non.silent_per_Mb,breaks=20,xlab="Non.silent TMB",main = "")
hist(df6$TMB_Silent_per_Mb,breaks=20,xlab="Silent TMB",main = "")
hist(data_feature$EMTscore,breaks=20,xlab="Transformed EMT score",main = "")
hist(data_feature$AGscore,breaks=20,xlab="Transformed Vascularization",main = "")
hist(data_feature$IFNGscore,breaks=20,xlab="Transformed IFNG score",main = "")
hist(data_feature$Leukocyte_fraction,breaks=20,xlab="Transformed Leukocyte fraction",main = "")
hist(data_feature$TMB_Non.silent_per_Mb,breaks=20,xlab="Transformed Non.silent TMB",main = "")
hist(data_feature$TMB_Silent_per_Mb,breaks=20,xlab="Transformed Silent TMB",main = "")
Bozorgui/imogene documentation built on Feb. 21, 2022, 11:57 a.m.
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#This code reproduces assessment of combinatorial interactions for T-cell dysfunction signature genes in Uterine Corpus Endometrial Carcinoma (UCEC).
# BAsed on ImogiMap package. Developed by Behnaz Bozorgui for the Korkut lab at MDA.
##Install from github-----------------------------
library(remotes)
install_github('korkutlab/imogimap')
library(imogimap)
#Input--------------------------------------------
#Set directory
current_dir <- dirname(rstudioapi::getSourceEditorContext()$path)
setwd(current_dir)
#UCEC T cell dysfunction genes
df_TCDF_genes<- read.csv("TCell_dys_genes_ucec.csv",header = T)
#Calculate and evaluate synergy scores------------
#Specificity and robustness evaluation of scores may take some time.
#To skip these evaluations set specificity and sensitivity(Robustness) to FALSE.
my_scores <- im_syng_tcga( onco_gene=df_TCDF_genes$Gene,
icp_gene = icp_gene_list,
cohort="ucec",
select_iap = "IFNGscore",
specificity = F,
sensitivity = F,
N_iteration_specificity = 1000,
N_iteration_sensitivity = 1000)
my_scores <- my_scores[-which(is.na(my_scores$Synergy_score)),]
#Compare-----------------------------------------
#To compare your results with ours
df_scores <- read.csv("TCell_dys_scores.csv")
df_scores <- df_scores[-which(is.na(df_scores$Synergy_score)),]
library(ggplot2)
library(circlize)
#Plot2A---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
while (!is.null(dev.list())) dev.off()
ggplot(df,aes(Synergy_score,r))+
geom_abline(slope =0,intercept = 1,color="red",size=2)+
geom_point(cex=1.5)+ theme_bw()+
xlim(c(-0.8,0.8))+
#ylim(c(-3,3))+
labs(title= "", x= "Synergy scores", y= "-log(R) Robustness",tag="1")+
guides(x.sec = "axis", y.sec = "axis") +
theme( axis.text.x = element_text(size=30,angle = 90),
axis.text.y = element_text(size=30,angle = 90),
axis.title = element_text(size=30),
plot.margin=unit(c(1,1,1.5,1.2),"cm"),
plot.tag.position = c(.05, 1.15),
plot.tag = element_text(size=60,colour="red",hjust = -0.3,vjust = 9),
axis.line.x.top = element_line( size = 1),
axis.line.x.bottom = element_line( size = 1),
axis.line.y.left = element_line( size = 1),
axis.line.y.right = element_line( size = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#Plot2B---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
df <- df[df$r>1,]
n <- nrow(df)
df$Q <- p.adjust(df$wilcox_pvalue,method="BH",n =n)
while (!is.null(dev.list())) dev.off()
par(mar = c(5,7,10,20))
ggplot(df,aes(Synergy_score,-log((Q))))+
geom_abline(slope =0,intercept = -log(0.1),color="red",size=2)+
geom_point(size=1.5)+ theme_bw()+xlim(c(-0.8,0.8))+
labs(title= "", x= "Synergy scores", y= " -log(Q) Significance\n",tag = "-log(0.1)")+
guides(x.sec = "axis", y.sec = "axis") +
theme( axis.text.x = element_text(size=30,angle = 90),
axis.text.y = element_text(size=30,angle = 90),
axis.title = element_text(size=30),
axis.line.x.top = element_line( size = 1),
axis.line.x.bottom = element_line( size = 1),
axis.line.y.left = element_line( size = 1),
axis.line.y.right = element_line( size = 1),
plot.margin=unit(c(1,1,1.5,1.2),"cm"),
plot.tag.position = c(.14, .4),
plot.tag = element_text(size=30,colour="red",hjust = -0.1,vjust = 1.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#Plot2C---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
df <- df[df$r>1,]
n <- nrow(df)
df$Q <- p.adjust(df$wilcox_pvalue,method="BH",n =n)
df <- df[df$Q<0.1,]
n<- nrow(df)
df$Q <- p.adjust(df$specificity_pvalue,method="BH",n =n)
while (!is.null(dev.list())) dev.off()
par(mar = c(5,7,10,20))
ggplot(df,aes(Synergy_score,-log((Q))))+
geom_abline(slope =0,intercept = -log(0.1),color="red",size=2)+
geom_point(size=2)+ theme_bw()+xlim(c(-0.8,0.8))+
guides(x.sec = "axis", y.sec = "axis") +
labs(title= "", x= "Synergy scores", y= "-log(Q) Specificity",tag = "-log(0.1)")+
theme( axis.text.x = element_text(size=30,angle = 90),
axis.text.y = element_text(size=30,angle = 90),
axis.title = element_text(size=30),
axis.line.x.top = element_line( size = 1),
axis.line.x.bottom = element_line( size = 1),
axis.line.y.left = element_line( size = 1),
axis.line.y.right = element_line( size = 1),
plot.margin=unit(c(1,1,1.5,1.2),"cm"),
plot.tag.position = c(.1, .65),
plot.tag = element_text(size=30,colour="red",hjust = -0.1,vjust = 1.),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
#plot2D---------------------------------------------
df <- df_scores
df$r <- -log(df$sensitivity_R)
df <- df[df$r>1,]
n <- nrow(df)
df$Q <- p.adjust(df$wilcox_pvalue,method="BH",n =n)
df <- df[df$Q<0.1,]
n<- nrow(df)
df$Q <- p.adjust(df$specificity_pvalue,method="BH",n =n)
df <- df[df$Q<0.1,]
#change seed to explore different layouts
while (!is.null(dev.list())) dev.off()
im_netplot(df =df, Immune_phenotype ="IFNGscore",cutoff = 0.,seed=3)
#Plot2E---------------------------------------------
while (!is.null(dev.list())) dev.off()
im_boxplot_tcga(onco_gene = "SERPINB9",
icp_gene = "CTLA4",
cohort = "ucec",
Immune_Feature ="IFNGscore",
logtrans = F)
dev.off()
#Plot2F---------------------------------------------
while (!is.null(dev.list())) dev.off()
im_boxplot_tcga(onco_gene = "CD70",
icp_gene = "CD86",
cohort = "ucec",
Immune_Feature ="IFNGscore",
logtrans = F)
#Plot2G---------------------------------------------
library(survival)
library(survminer)
#Read TCGA UCEC survival values
#Survival values are taken from Liu J., et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173,400 (2018).
clin <- read.csv("TCGA_PFIsurvival_UCEC.csv",header = T)
#Read TCGA UCEC expression
df <-curatedTCGAData::curatedTCGAData( diseaseCode = "ucec",
assays = c("RNASeq2GeneNorm"),
dry.run = F)@ExperimentList@listData[[1]]
data_expression <- df@assays$data@listData[[1]]
colnames(data_expression)<- substr(colnames(data_expression), 1, 15)
#Select genes
df_selected <- t(data_expression[rownames(data_expression ) %in% c("CD86","CD70"),])
#Log2p1 transform
df_selected <- scale(log2(df_selected+1),center = T,scale = T)
#Take mean of multiple samples
df_selected <- as.data.frame(df_selected)
df_selected$bcr_patient_barcode <- substr(rownames(df_selected), 1, 12)
df_selected <- aggregate(list(df_selected$CD70,df_selected$CD86),
list(df_selected$bcr_patient_barcode), FUN=mean)
rownames(df_selected)<- df_selected$Group.1
df_selected$Group.1<-NULL
colnames(df_selected)<- c("CD70","CD86")
df_selected <- as.matrix(df_selected)
#Get quantile ranks for each sample
df_select_qr <- get_quantile_rank(df_selected)
df_select_qr$bcr_patient_barcode <- df_select_qr$Tumor_Sample_ID
#Select samples with expression values in Q1 or Q3 quantile.
df_select_qr <- cbind(df_select_qr,as.integer((df_select_qr[,2]*2+df_select_qr[,3])/3))
df_select_qr <- df_select_qr[df_select_qr[,5]%in% c(1,4),]
colnames(df_select_qr)[5]<-"status"
#Merge data
df_surv <- merge(df_select_qr ,clin,by="bcr_patient_barcode")
df_surv$pcevent <- df_surv$PFI
df_surv$pctime <- df_surv$PFI.time
df_surv <- df_surv[!is.na(df_surv$pctime),]
df_surv <- df_surv[,c("Tumor_Sample_ID","pcevent","pctime","status")]
#Survival analysis
cox1 <- coxph( Surv(pctime,pcevent) ~ status , data = df_surv )
hr <- exp(cox1$coefficients)
fit1 <- survfit( Surv(pctime,pcevent) ~ status , data = df_surv ,type="kaplan-meier")
pval <- surv_pvalue(
fit1,
data = df_surv,
method = "Log-rank"
)$pval
#plot
while (!is.null(dev.list())) dev.off()
ggsurv <- ggsurvplot(fit1, data = df_surv, risk.table=F, size = 2,
palette = c("blue","red"),
xlab="Time (Days)", legend.title="",
legend.labs = c("Low CD86 Low CD70","High CD86 High CD70"),
legend=c(0.7,0.4),font.legend=30,
#pval=T,pval.method=T,
font.x=30,font.y=30,font.tickslab=30)
ggsurv$plot <- ggsurv$plot +
ggplot2::annotate(
"text",
x = 0, y = 0,
vjust = 0.1, hjust = 0,
label = paste0("Log-Rank Pvalue", pval ,"\nHazard Ratio ,",hr),
size = 10
)
ggsurv
#Supplementary figure----------------------------------------
df_all<-list()
#Read TCGA mRNA data for all disease cohorts
for(cohortID in 1:length(TCGA_disease_list)){
disease <- TCGA_disease_list[cohortID]
message("\nReading TCGA ",toupper(disease)," data\n")
df <-curatedTCGAData::curatedTCGAData( diseaseCode =disease,
assays = c("RNASeq2GeneNorm"), dry.run = F)@ExperimentList@listData[[1]]
df <- df@assays$data@listData[[1]]
colnames(df)<- substr(colnames(df), 1, 15)
df_all[[cohortID]] <- df
}
df <- lapply(df_all, as.data.frame)
df <- lapply(df, function(x){x$gene <- rownames(x); x})
df <-Reduce(merge,df)
rownames(df)<-df$gene
df$gene<-NULL
#Calculate IAP values
df1 <- get_emt_score(df)
df2 <- get_angio_score(df)
df3 <- get_ifng_score(df)
df4 <-TCGA_Leukocyte_fraction
df5 <-TCGA_TMB[,c("Tumor_Sample_ID","TMB_Non.silent_per_Mb")]
df6 <- TCGA_TMB[,c("Tumor_Sample_ID","TMB_Silent_per_Mb")]
data_feature <- get_features(df)
d1<- merge(df1, data_feature[,c("Tumor_Sample_ID" ,"EMTscore")],by= "Tumor_Sample_ID")
d2<- merge(df2, data_feature[,c("Tumor_Sample_ID" ,"AGscore")],by= "Tumor_Sample_ID")
d3<- merge(df3, data_feature[,c("Tumor_Sample_ID" ,"IFNGscore")],by= "Tumor_Sample_ID")
d4<- merge(df4, data_feature[,c("Tumor_Sample_ID" ,"Leukocyte_fraction")],by= "Tumor_Sample_ID")
d5<- merge(df5, data_feature[,c("Tumor_Sample_ID" ,"TMB_Non.silent_per_Mb")],by= "Tumor_Sample_ID")
d6<- merge(df6, data_feature[,c("Tumor_Sample_ID" ,"TMB_Silent_per_Mb")],by= "Tumor_Sample_ID")
#plot
while (!is.null(dev.list())) dev.off()
plot(d1$EMTscore.x,d1$EMTscore.y,
xlab="EMT score",ylab="Transformed\n EMT score")
plot(d2$AGscore.x,d2$AGscore.y,
xlab="Vascularization",ylab="Transformed\n Vascularization")
plot(d3$IFNGscore.x,d3$IFNGscore.y,
xlab="IFNG score",ylab="Transformed\n IFNG score")
plot(d4$Leukocyte_fraction.x,d4$Leukocyte_fraction.y,
xlab="Leukocyte fraction",ylab="Transformed\n Leukocyte fraction")
plot(d5$TMB_Non.silent_per_Mb.x,d5$TMB_Non.silent_per_Mb.y,
xlab="Non.silent TMB",ylab="Transformed\n Non.silent TMB")
plot(d6$TMB_Silent_per_Mb.x,d6$TMB_Silent_per_Mb.y,
xlab="Silent TMB",ylab="Transformed\n Silent TMB")
hist(df1$EMTscore,breaks=20,xlab="EMT score",main = "")
hist(df2$AGscore,breaks=20,xlab="Vascularization",main = "")
hist(df3$IFNGscore,breaks=20,xlab="IFNG score",main = "")
hist(df4$Leukocyte_fraction,breaks=20,xlab="Leukocyte fraction",main = "")
hist(df5$TMB_Non.silent_per_Mb,breaks=20,xlab="Non.silent TMB",main = "")
hist(df6$TMB_Silent_per_Mb,breaks=20,xlab="Silent TMB",main = "")
hist(data_feature$EMTscore,breaks=20,xlab="Transformed EMT score",main = "")
hist(data_feature$AGscore,breaks=20,xlab="Transformed Vascularization",main = "")
hist(data_feature$IFNGscore,breaks=20,xlab="Transformed IFNG score",main = "")
hist(data_feature$Leukocyte_fraction,breaks=20,xlab="Transformed Leukocyte fraction",main = "")
hist(data_feature$TMB_Non.silent_per_Mb,breaks=20,xlab="Transformed Non.silent TMB",main = "")
hist(data_feature$TMB_Silent_per_Mb,breaks=20,xlab="Transformed Silent TMB",main = "")
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