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Project.Rmd
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---
title: "Pan-cancer analyses reveal molecular and clinical characteristics of cuproptosis regulators"
author: "Changwu Wu, Jun Tan, Xiangyu Wang, Chaoying Qin, Wenyong Long, Yimin Pan, Yuzhe Li, Qing Liu"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
df_print: paged
theme: cerulean
highlight: haddock
toc: yes
toc_depth: 3
toc_float:
collapsed: false
smooth_scroll: true
code_fold: show
---
```{r, echo=F}
knitr::opts_chunk$set( echo = TRUE, message=FALSE, warning=FALSE, fig.width=8 )
```
# Check if BiocManager package installed?
```{r}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
```
# Somatic alteration landscape of cuproptosis regulators
## Input file preparation
'mc3.v0.2.8.PUBLICtxt.txt' and 'CopyNumber.txt': These files are the SNV and CNV data of pan cancer from TCGA. They can be downlaod from XENA. A screenshot is listed below to show the step
```{r}
knitr::include_graphics("SNV_CNV/images/SNV_CNV_Download.png")
```
### extract SNV and CNV data of cuproptosis genes
```{r}
mc3_XENA=read.table("SNV_CNV/mc3.v0.2.8.PUBLICtxt.txt",header = T,sep = '\t',check.names = F)
mc3_cu=mc3_XENA[grep(pattern="FDX1|LIAS|LIPT1|DLD|DLAT|PDHA1|PDHB|MTF1|GLS|CDKN2A",mc3_XENA[,7]),]
write.table(mc3_cu,"mc3_cu.txt",quote = F,sep = "\t",row.names = F)
CNV_XENA=read.table("SNV_CNV/CopyNumber.txt",header = T,sep = '\t',check.names = F)
CNV_XENA=CNV_XENA[grep(pattern="FDX1|LIAS|LIPT1|DLD|DLAT|PDHA1|PDHB|MTF1|GLS|CDKN2A",CNV_XENA[,1]),]
write.table(CNV_XENA,"CNV_cu.txt",quote = F,sep = "\t",row.names = F)
```
`SNV.xlsx`: This file is the SNV data of cuproptosis regulators. Sheet1 is the whole data. Sheet2-10 are the data of every gene.
This data is manually generated based on "mc_cu.txt" from the above step.Screenshots are listed below to show other steps in excel.
```{r}
knitr::include_graphics(c("SNV_CNV/images/SNV_1.png", "SNV_CNV/images/SNV_2.png"))
```
`CNV.xlsx`: This file is the SNV data of cuproptosis regulators. Sheet1 is the whole data. Sheet2 is the data with tumor type.This data is manually generated based on "CNV_cu.txt" from the above step.Screenshots are listed below to show other steps in excel.
```{r}
knitr::include_graphics(c("SNV_CNV/images/CNV_1.png", "SNV_CNV/images/CNV_2.png"))
```
`tumortype.xlsx`: This is the clinical data of pan cancer from TCGA. It can be got from XENA. A screenshot is listed below to show the step
```{r}
knitr::include_graphics("SNV_CNV/images/clinical_download.png")
```
`amplification.txt`/`deletion.txt`/`mutation.txt`: This is the amplification/deletion/mutation percentage of cuproptosis regulators. It is generated manually in excel. Three screenshots are listed below to show the steps
```{r}
knitr::include_graphics(c("SNV_CNV/images/percentage_1.png", "SNV_CNV/images/percentage_2.png","SNV_CNV/images/percentage_3.png"))
```
## Figure 2 and related plots
### Install required packages
```{r}
a = rownames(installed.packages())
install_bioc <- c("openxlsx", "dplyr", "ComplexHeatmap", "circlize")
for (i in install_bioc) {
if (!i %in% a)
BiocManager::install(i, update = F, site_repository = site)
a = rownames(installed.packages())
}
```
#### Mutation profile (not shown in manuscript)
```{r}
library(openxlsx)
FDX1 <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet2")
LIAS <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet3")
LIPT1 <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet4")
DLD <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet5")
DLAT <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet6")
PDHA1 <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet7")
PDHB <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet8")
MTF1 <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet9")
GLS <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet10")
CDKN2A <- read.xlsx("SNV_CNV/SNV.xlsx", sheet = "Sheet11")
tumortype <- read.xlsx("SNV_CNV/tumortype.xlsx", sheet = "Sheet1")
library(dplyr)
SNV = dplyr::left_join(tumortype, FDX1, by = "sample")
SNV = dplyr::left_join(SNV, LIAS, by = "sample")
SNV = dplyr::left_join(SNV, LIPT1, by = "sample")
SNV = dplyr::left_join(SNV, DLD, by = "sample")
SNV = dplyr::left_join(SNV, DLAT, by = "sample")
SNV = dplyr::left_join(SNV, PDHA1, by = "sample")
SNV = dplyr::left_join(SNV, PDHB, by = "sample")
SNV = dplyr::left_join(SNV, MTF1, by = "sample")
SNV = dplyr::left_join(SNV, GLS, by = "sample")
SNV = dplyr::left_join(SNV, CDKN2A, by = "sample")
write.table(
SNV,
"SNV_CNV/SNV2.txt",
sep = "\t",
quote = FALSE,
col.names = TRUE,
row.names = F
)
library(ComplexHeatmap)
library(circlize)
#SNV
mut <- SNV[, -2]
cli <- SNV
rownames(mut) <- mut$sample
mat <- mut[, -1]
mat[is.na(mat)] <- ""
mat[1:6, 1:6]
mat = t(mat)
mat[1:6, 1:6]
######
col <-
c(
"Missense_Mutation" = "#27AE60",
"Nonsense_Mutation" = "blue",
"Nonstop_Mutation" = "#B452CD",
"Splice_Site" = "red",
"In_Frame_Del" = "#EEC900",
"Frame_Shift_Del" = "#FF6A6A",
"Frame_Shift_Ins" = "#9400D3",
"Mutiple" = "black"
)
alter_fun <- list(
background = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = "#e8e7e3", col = NA))
},
Missense_Mutation = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Missense_Mutation"], col = NA))
},
Nonsense_Mutation = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Nonsense_Mutation"], col = NA))
},
Nonstop_Mutation = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Nonstop_Mutation"], col = NA))
},
Splice_Site = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Splice_Site"], col = NA))
},
In_Frame_Del = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["In_Frame_Del"], col = NA))
},
Frame_Shift_Del = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Frame_Shift_Del"], col = NA))
},
Frame_Shift_Ins = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Frame_Shift_Ins"], col = NA))
},
Mutiple = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.3,
gp = gpar(fill = col["Mutiple"], col = NA))
}
)
heatmap_legend <- list(
title = "SNV",
at = c(
"Missense_Mutation",
"Nonsense_Mutation",
"Nonstop_Mutation",
"Splice_Site",
"In_Frame_Del",
"Frame_Shift_Del",
"Frame_Shift_Ins",
"Mutiple"
),
labels = c(
"Missense_Mutation",
"Nonsense_Mutation",
"Nonstop_Mutation",
"Splice_Site",
"In_Frame_Del",
"Frame_Shift_Del",
"Frame_Shift_Ins",
"Mutiple"
)
)
oncoPrint(
mat ,
alter_fun = alter_fun,
col = col,
#column_title = column_title,
remove_empty_columns = T,
remove_empty_rows = T,
row_names_side = "left",
pct_side = "right",
alter_fun_is_vectorized = FALSE,
heatmap_legend_param = heatmap_legend,
)
```
Add cancer type to mutation profile.
```{r}
pdata <- cli
head(pdata)
ha <- HeatmapAnnotation(
Cancer_type = pdata$Cancer.type,
show_annotation_name = TRUE,
annotation_name_gp = gpar(fontsize = 7)
)
oncoPrint(
mat ,
alter_fun = alter_fun,
col = col,
remove_empty_columns = T,
remove_empty_rows = T,
row_names_side = "left",
pct_side = "right",
alter_fun_is_vectorized = FALSE,
heatmap_legend_param = heatmap_legend,
bottom_annotation = ha
)
#自定义样本顺序
s <- pdata[order(pdata$Cancer.type, pdata$CDKN2A, pdata$DLD, pdata$MTF1), ]
sample_order <- as.character(s$sample)
#自定义颜色
ha <- HeatmapAnnotation(
Cancer_type = pdata$Cancer.type,
col = list(
Cancer_type = c(
"ACC" = "#FFB6C1",
"BLCA" = "#4E72B8",
"BRCA" = "#FAEBD7",
"CESC" = "#00FFFF",
"CHOL" = "#7FFFAA",
"COAD" = "#FFE4C4",
"DLBC" = "#0000FF",
"ESCA" = "#8A2BE2",
"GBM" = "#A52A2A",
"HNSC" = "#7FFF00",
"KICH" = "#5F9EA0",
"KIRC" = "#DEB887",
"KIRP" = "#FF7F50",
"LAML" = "#6495ED",
"LGG" = "#FFF8DC",
"LIHC" = "#00008B",
"LUAD" = "#CD5C5C",
"LUSC" = "#A9A9A9",
"MESO" = "#8B008B",
"OV" = "#FF8C00",
"PAAD" = "#E9967A",
"PCPG" = "#FF1493",
"PRAD" = "#00BFFF",
"READ" = "#FFFAF0",
"SARC" = "#FFD700",
"SKCM" = "#FF6347",
"STAD" = "#FF69B4",
"TGCT" = "#66CDAA",
"THCA" = "#BA55D3",
"THYM" = "#FF4500",
"UCEC" = "#DDA0DD",
"UCS" = "#008B8B",
"UVM" = "#FFFF00"
)
),
show_annotation_name = TRUE,
annotation_name_gp = gpar(fontsize = 7)
)
```
```{r}
oncoPrint(
mat ,
alter_fun = alter_fun,
col = col,
remove_empty_columns = T,
remove_empty_rows = T,
column_order = sample_order,
row_names_side = "left",
pct_side = "right",
alter_fun_is_vectorized = FALSE,
heatmap_legend_param = heatmap_legend,
bottom_annotation = ha
)
##8.5*4.5
```
#### CNV profile (Figure 2A)
```{r}
#CNV
mut <- read.xlsx("SNV_CNV/CNV.xlsx", sheet = "Sheet1")
cli <- read.xlsx("SNV_CNV/CNV.xlsx", sheet = "Sheet2")
rownames(mut) <- mut$sample
mat <- mut[, -1]
mat[is.na(mat)] <- ""
mat[1:6, 1:6]
mat = t(mat)
mat[1:6, 1:6]
######画图
col <- c("Deep deletion" = "blue",
"Amplification" = "red")
alter_fun <- list(
background = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = "#e8e7e3", col = NA))
},
Amplification = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Amplification"], col = NA))
},
"Deep deletion" = function(x, y, w, h) {
grid.rect(x, y, w - unit(0.5, "mm"), h * 0.7,
gp = gpar(fill = col["Deep deletion"], col = NA))
}
)
heatmap_legend <- list(
title = "CNV",
at = c("Amplification", "Deep deletion"),
labels = c("Amplification", "Deep deletion")
)
oncoPrint(
mat ,
alter_fun = alter_fun,
col = col,
#column_title = column_title,
remove_empty_columns = T,
remove_empty_rows = T,
row_names_side = "left",
pct_side = "right",
alter_fun_is_vectorized = FALSE,
heatmap_legend_param = heatmap_legend,
)
pdata <- cli
head(pdata)
ha <- HeatmapAnnotation(
Cancer_type = pdata$Cancer.type,
show_annotation_name = TRUE,
annotation_name_gp = gpar(fontsize = 7)
)
oncoPrint(
mat ,
alter_fun = alter_fun,
col = col,
remove_empty_columns = T,
remove_empty_rows = T,
row_names_side = "left",
pct_side = "right",
alter_fun_is_vectorized = FALSE,
heatmap_legend_param = heatmap_legend,
bottom_annotation = ha
)
s <-
pdata[order(pdata$Cancer.type, pdata$CDKN2A, pdata$DLD, pdata$MTF1), ]
sample_order <- as.character(s$sample)
ha <- HeatmapAnnotation(
Cancer_type = pdata$Cancer.type,
col = list(
Cancer_type = c(
"ACC" = "#FFB6C1",
"BLCA" = "#4E72B8",
"BRCA" = "#FAEBD7"
,
"CESC" = "#00FFFF",
"CHOL" = "#7FFFAA",
"COAD" = "#FFE4C4"
,
"DLBC" = "#0000FF",
"ESCA" = "#8A2BE2",
"GBM" = "#A52A2A"
,
"HNSC" = "#7FFF00",
"KICH" = "#5F9EA0",
"KIRC" = "#DEB887"
,
"KIRP" = "#FF7F50",
"LAML" = "#6495ED",
"LGG" = "#FFF8DC"
,
"LIHC" = "#00008B",
"LUAD" = "#CD5C5C",
"LUSC" = "#A9A9A9"
,
"MESO" = "#8B008B",
"OV" = "#FF8C00",
"PAAD" = "#E9967A"
,
"PCPG" = "#FF1493",
"PRAD" = "#00BFFF",
"READ" = "#FFFAF0"
,
"SARC" = "#FFD700",
"SKCM" = "#FF6347",
"STAD" = "#FF69B4"
,
"TGCT" = "#66CDAA",
"THCA" = "#BA55D3",
"THYM" = "#FF4500"
,
"UCEC" = "#DDA0DD",
"UCS" = "#008B8B",
"UVM" = "#FFFF00"
)
),
show_annotation_name = TRUE,
annotation_name_gp = gpar(fontsize = 7)
)
oncoPrint(
mat ,
alter_fun = alter_fun,
col = col,
remove_empty_columns = T,
remove_empty_rows = T,
column_order = sample_order,
row_names_side = "left",
pct_side = "right",
alter_fun_is_vectorized = FALSE,
heatmap_legend_param = heatmap_legend,
bottom_annotation = ha
)
##9*4.5
```
#### Mutation percentage (Figure 2B)
```{r}
mutation=read.table(file = "SNV_CNV/mutation.txt",sep = "\t",header = T,check.names = F,row.names = 1)
col_fun = colorRamp2(c(0, 10), c("white", "#3CB371"))
mutation[is.na(mutation)] =0
Heatmap(mutation,cluster_rows = F, cluster_columns = F,top_annotation = NULL,
show_column_dend=F,column_title="Mutation",column_title_side="top",
show_column_names=T,border = T,col = col_fun,column_names_rot = 45,
na_col = "white",
cell_fun = function(j, i, x, y, width, height, fill) {
if(mutation[i, j] >0)
grid.text(sprintf("%.1f", mutation[i, j]), x, y, gp = gpar(fontsize = 13))})
```
#### amplification percentage (Figure 2C)
```{r}
amplification=read.table(file = "SNV_CNV/amplification.txt",sep = "\t",header = T,check.names = F,row.names = 1)
col_fun = colorRamp2(c(0, 8), c("white", "red"))
amplification[is.na(amplification)] =0
Heatmap(amplification,cluster_rows = F, cluster_columns = F,top_annotation = NULL,
show_column_dend=F,column_title="Amplification",column_title_side="top",
show_column_names=T,border = T,col = col_fun,column_names_rot = 45,
na_col = "white",
cell_fun = function(j, i, x, y, width, height, fill) {
if(amplification[i, j] >0)
grid.text(sprintf("%.1f", amplification[i, j]), x, y, gp = gpar(fontsize = 10))})
```
#### deletion percentage (Figure 2D)
```{r}
deletion=read.table(file = "SNV_CNV/deletion.txt",sep = "\t",header = T,check.names = F,row.names = 1)
col_fun = colorRamp2(c(0, 60), c("white", "#4169E1"))
deletion[is.na(deletion)] =0
Heatmap(deletion,cluster_rows = F, cluster_columns = F,top_annotation = NULL,
show_column_dend=F,column_title="Deep deletion",column_title_side="top",
show_column_names=T,border = T,col = col_fun,column_names_rot = 45,
na_col = "white",
cell_fun = function(j, i, x, y, width, height, fill) {
if(deletion[i, j] >0)
grid.text(sprintf("%.1f", deletion[i, j]), x, y, gp = gpar(fontsize = 13))})
```
# mRNA expression analysis and cuproptosis scores
## Input file preparation
'RNASeqV2': This file is the mRNA data of pan cancer from TCGA. It can be downlaod from XENA. A screenshot is listed below to show the step. It can also be downloaded in : https://pan.baidu.com/s/1yH7bxmmQTYTb-s5EnCWrKg
keyword：p3it
```{r}
knitr::include_graphics("mRNA/images/mRNA_downlaod.png")
```
'CNV_cu.txt':This file is the CNV data of cuproptosis regulators. It is from the above CNV analysis.
'RNA_cu.txt': This file is the mRNA expression of cuproptosis regulators. It is extracted from RNASeq data of pancancer.
'RNA_cu_clinical.txt': This file is the mRNA expression of cuproptosis regulators with clinical information. Just need to merge mRNA expression and clinical data.
'T_N.txt': This file is patients with tumor and nomral samples. It can be filtered from clinical data file.A screenshot is listed below to show the step. Then choose the patients with tumor and paired nomral samples.
```{r}
knitr::include_graphics("mRNA/images/TN.png")
```
'XENA_clinical.txt': Clinical data. Obtained from XENA
```{r}
knitr::include_graphics("mRNA/images/clinical_download.png")
```
'T_N_cu.txt': The cuproptosis regulators expression in tumor and paried normal sapmles. It is generated by mergeing cuproptosis regulators expression in all samples and tumor-normal sample list.
'cuproptosis_genesets.txt':It is the geneset of cuproptosis, which if from the study of Tsvetkov et al.(DOI: 10.1126/science.abf0529)
'hallmark_cu_genesets.txt': It is the geneset of hallmark. It is from MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/. The steps are here.
```{r}
knitr::include_graphics(c("GSVA/images/hallmark_1.png", "GSVA/images/hallmark_2.png"))
```
'RNASeq2_gsva_cu': The cuproptosis score of all samples. It is generated by GSVA.
'RNASeq2_gsva_hallmark': The hallmark score of all samples. It is generated by GSVA.
'RNC_cu_pancan.txt': The cuproptosis regulators expresssion in pan cancer. It is extracted from RNA seq data.
'k2.txt': The cluster of all samples. It is generated by cluster analysis.
'RNA_cu_clinical_cuscore.txt': The file is clinical data, cuproptosis score, hallmark score and cuproptosis regulators expression. Just need to merge them all to generate this file.
'cluster_distribution.txt': The tumor distribution data of clusters. It is genrated by calculating the percentage of tumor samples.
```{r}
knitr::include_graphics(c("Cluster/images/Cluster_1.png", "Cluster/images/Cluster_2.png"))
```
## Figure 3 and related plots
### Install required packages
```{r}
a = rownames(installed.packages())
install_bioc <- c("data.table", "Hmisc", "corrplot","Biobase","genefilter","RColorBrewer","GSVA","pheatmap","ConsensusClusterPlus","DOSE","org.Hs.eg.db","topGO","clusterProfiler","pathview")
for (i in install_bioc) {
if (!i %in% a)
BiocManager::install(i, update = F, site_repository = site)
a = rownames(installed.packages())
}
```
### relationship between CNV and mRNA expression (not shown in manuscript)
```{r}
###prepare data
library(data.table)
RNASeq=fread("mRNA/RNASeqV2.txt", data.table=FALSE)
RNASeq=RNASeq[!duplicated(RNASeq$sample),]
rownames(RNASeq)=RNASeq$sample
cu_RNASeq=RNASeq[c("FDX1","LIAS","LIPT1","DLD","DLAT","PDHA1","PDHB","MTF1","GLS","CDKN2A"),]
write.table(cu_RNASeq,"cu_RNASeq.txt",quote = F,sep = "\t",col.names = T,row.names = F)
na.omit(RNASeq)[1:4,1:4]
RNASeq2=na.omit(RNASeq)
write.table(RNASeq2,"RNASeq2.txt",quote = F,sep = "\t",col.names = T,row.names = F)
RNA_cu=t(cu_RNASeq[,-1])
RNA_cu=as.data.frame(RNA_cu)
RNA_cu$sample=rownames(RNA_cu)
T_N=read.table(file = "mRNA/T_N.txt",sep = "\t",header = T,check.names = F)
T_N_cu1=merge(T_N,RNA_cu,by="sample")
#####combine CNV and mRNA expression
RNA_cu <- read.table(file = "mRNA/RNA_cu.txt",sep = "\t",header = T,check.names = F)
CNV_cu=read.table(file = "mRNA/CNV_cu.txt",sep = "\t",header = T,check.names = F)
XENA_clinical=read.table(file = "mRNA/XENA_clinical.txt",sep = "\t",header = T,check.names = F)
colnames(CNV_cu)[1]="sample"
RNA_CNV=merge(RNA_cu,CNV_cu,by="sample")
RNA_CNV_clinical=merge(RNA_CNV,XENA_clinical,by="sample")
write.table(RNA_CNV_clinical,"RNA_CNV_clinical.txt",quote = F,sep = "\t",col.names = T,row.names = F)
#####Using "RNA_CNV_clinical" to plot box plots of CNV/mRNA in sangerbox. Please cite:Shen, et al. 2022. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta 1(3): e36. https://doi.org/10.1002/imt2.36
```
###Differential expression analysis of paired normal and tumor tissues (Figure 3A)
```{r}
##mRNA expression fold change
T_N<- read.table(file = "mRNA/T_N_cu.txt",sep = "\t",header = T,check.names = F)
indata=t(T_N[,c(1,5,6,7,8,9,10,11,12,13,14)])
indata=as.data.frame(indata)
colnames(indata)=indata[1,]
indata=indata[-1,]
##BLCA
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="BLCA");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="BLCA");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
BLCA=cbind(rownames(indata),result)
colnames(BLCA)[1]="SYMBOL"
colnames(BLCA)[2]="BLCA"
##BRCA
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="BRCA");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="BRCA");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
BRCA=cbind(rownames(indata),result)
colnames(BRCA)[1]="SYMBOL"
colnames(BRCA)[2]="BRCA"
##CHOL
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="CHOL");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="CHOL");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
CHOL=cbind(rownames(indata),result)
colnames(CHOL)[1]="SYMBOL"
colnames(CHOL)[2]="CHOL"
##COAD
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="COAD");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="COAD");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
COAD=cbind(rownames(indata),result)
colnames(COAD)[1]="SYMBOL"
colnames(COAD)[2]="COAD"
##ESCA
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="ESCA");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="ESCA");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
ESCA=cbind(rownames(indata),result)
colnames(ESCA)[1]="SYMBOL"
colnames(ESCA)[2]="ESCA"
##HNSC
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="HNSC");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="HNSC");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
HNSC=cbind(rownames(indata),result)
colnames(HNSC)[1]="SYMBOL"
colnames(HNSC)[2]="HNSC"
##KICH
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="KICH");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="KICH");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
KICH=cbind(rownames(indata),result)
colnames(KICH)[1]="SYMBOL"
colnames(KICH)[2]="KICH"
##KIRC
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="KIRC");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="KIRC");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
KIRC=cbind(rownames(indata),result)
colnames(KIRC)[1]="SYMBOL"
colnames(KIRC)[2]="KIRC"
##KIRP
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="KIRP");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="KIRP");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
KIRP=cbind(rownames(indata),result)
colnames(KIRP)[1]="SYMBOL"
colnames(KIRP)[2]="KIRP"
##LIHC
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="LIHC");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="LIHC");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
LIHC=cbind(rownames(indata),result)
colnames(LIHC)[1]="SYMBOL"
colnames(LIHC)[2]="LIHC"
##LUAD
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="LUAD");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="LUAD");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
LUAD=cbind(rownames(indata),result)
colnames(LUAD)[1]="SYMBOL"
colnames(LUAD)[2]="LUAD"
##LUSC
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="LUSC");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="LUSC");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
LUSC=cbind(rownames(indata),result)
colnames(LUSC)[1]="SYMBOL"
colnames(LUSC)[2]="LUSC"
##PRAD
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="PRAD");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="PRAD");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
PRAD=cbind(rownames(indata),result)
colnames(PRAD)[1]="SYMBOL"
colnames(PRAD)[2]="PRAD"
##READ
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="READ");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="READ");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
READ=cbind(rownames(indata),result)
colnames(READ)[1]="SYMBOL"
colnames(READ)[2]="READ"
##STAD
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="STAD");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="STAD");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
STAD=cbind(rownames(indata),result)
colnames(STAD)[1]="SYMBOL"
colnames(STAD)[2]="STAD"
##THCA
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="THCA");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="THCA");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
THCA=cbind(rownames(indata),result)
colnames(THCA)[1]="SYMBOL"
colnames(THCA)[2]="THCA"
##UCEC
sampleIdsCase<-which(T_N$TN=="tumor"&T_N$cancer=="UCEC");
sampleIdsControl<-which(T_N$TN=="normal"&T_N$cancer=="UCEC");
probeFC<-rep(0,nrow(indata))
for(i in 1:nrow(indata))
{
probeFC[i]<-mean(as.numeric(as.vector(indata[i,sampleIdsCase])))/mean(as.numeric(as.vector(indata[i,sampleIdsControl])));
}
probeFC<-log(probeFC,base=2);
result<-probeFC;
UCEC=cbind(rownames(indata),result)
colnames(UCEC)[1]="SYMBOL"
colnames(UCEC)[2]="UCEC"
TN_CU=cbind(BLCA,BRCA,CHOL,COAD,ESCA,HNSC,KICH,KIRC,KIRP,LIHC,LUAD,LUSC,PRAD,READ,STAD,THCA,UCEC)
write.table(TN_CU, file = "TN_CU_FC.txt", sep = "\t", row.names = F,col.names = T, quote = F)
###TN_CU_FC is the Fold change results
####Using sangerbox to caculate significance and visualize, the stpes are below
knitr::include_graphics(c("mRNA/images/significance1.png", "mRNA/images/significance2.png","mRNA/images/FC1.png","mRNA/images/FC2.png"))
```
### GSVA for cuproptosis scores and hallmark score (not shown in manuscript)
```{r}
library(data.table)
RNASeq2=fread("RNASeq2.txt", data.table=FALSE)
rownames(RNASeq2)=RNASeq2$sample
RNASeq2=RNASeq2[,-1]
#######GSVA
library(Biobase)
library(genefilter)
library(RColorBrewer)
library(GSVA)
library(pheatmap)
##########input data ,construct gmt for hallmark
##
hallmark_cu_genesets<- read.table(file = "GSVA/hallmark_cu_genesets.txt",sep = "\t",header = T,check.names = F)
sel_gmt=hallmark_cu_genesets
head(sel_gmt)
dim(sel_gmt)
sets=as.list(sel_gmt)
sets=lapply(sets, function(x) x[!is.na(x)])
#sets[1]
#########calculation GSVA score
exprMatrix=RNASeq2
head(exprMatrix)
dim(exprMatrix)
gsva_matrix<- gsva(as.matrix(exprMatrix), sets,method='gsva',kcdf='Gaussian',abs.ranking=F)
gsva_matrix1<- t(scale(t(gsva_matrix)))
#head(gsva_matrix1)
normalization<-function(x){
return((x-min(x))/(max(x)-min(x)))}
nor_gsva_matrix1 <- normalization(gsva_matrix1)
dim(nor_gsva_matrix1)
score.gsva.hallmark=as.data.frame(t(nor_gsva_matrix1))
head(score.gsva.hallmark)
##########input data ,construct gmt for cuproptosis score
##
cuproptosis_genesets<- read.table(file = "GSVA/cuproptosis_genesets.txt",sep = "\t",header = T,check.names = F)
sel_gmt=cuproptosis_genesets
head(sel_gmt)
dim(sel_gmt)
sets=as.list(sel_gmt)
sets=lapply(sets, function(x) x[!is.na(x)])
#sets[1]
#########calculation GSVA score
exprMatrix=RNASeq2
head(exprMatrix)
dim(exprMatrix)
gsva_matrix<- gsva(as.matrix(exprMatrix), sets,method='gsva',kcdf='Gaussian',abs.ranking=F)
gsva_matrix1<- t(scale(t(gsva_matrix)))
#head(gsva_matrix1)
normalization<-function(x){
return((x-min(x))/(max(x)-min(x)))}
nor_gsva_matrix1 <- normalization(gsva_matrix1)
dim(nor_gsva_matrix1)
score.gsva.cu=as.data.frame(t(nor_gsva_matrix1))
head(score.gsva.cu)
```
###correlation among cuproptosis scores and regulators(not shown in manuscript)