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TCGA_pro_clin_norm.R
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TCGA_pro_clin_norm.R
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library(glmSparseNet)
library(curatedTCGAData)
library(TCGAutils)
library(dplyr)
# 设置路径 --------------------------------------------------------------------
# Extract expression matrix of TNBC subtypes of the BRCA dataset of the TCGA database ------------------------------------------
# Construct survival analysis model, pure tumor sample expression matrix, survival information of corresponding tumor patient
# params <- list(seed = 29221)
brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm", FALSE)
head(getSubtypeMap(brca))
head(getClinicalNames("BRCA"))
brca.primary.solid.tumor <- TCGAutils::splitAssays(brca, '01')
xdata.raw <- t(assay(brca.primary.solid.tumor[[1]]))
dim(xdata.raw) # 1093 20501
xdata.raw <- xdata.raw %>%
{ (apply(., 2, sd) != 0) } %>%
{ xdata.raw[, .] }
dim(xdata.raw) # 1093 20220
# Get survival information
ydata.raw <- colData(brca.primary.solid.tumor) %>% as.data.frame %>%
# Keep only data relative to survival or samples
select(patientID, vital_status,
Days.to.date.of.Death, Days.to.Date.of.Last.Contact,
days_to_death, days_to_last_followup,
Vital.Status) %>%
# Convert days to integer
mutate(Days.to.date.of.Death = as.integer(Days.to.date.of.Death)) %>%
mutate(Days.to.Last.Contact = as.integer(Days.to.Date.of.Last.Contact)) %>%
# Find max time between all days (ignoring missings)
rowwise %>%
mutate(time = max(days_to_last_followup, Days.to.date.of.Death,
Days.to.Last.Contact, days_to_death, na.rm = TRUE)) %>%
# Keep only survival variables and codes
select(patientID, status = vital_status, time) %>%
# Discard individuals with survival time less or equal to 0
filter(!is.na(time) & time > 0) %>% as.data.frame
dim(ydata.raw) # 1080 3
# Set index as the patientID
rownames(ydata.raw) <- ydata.raw$patientID
# Get matches between survival and assay data
xdata.raw_1 <- xdata.raw[TCGAbarcode(rownames(xdata.raw)) %in%
rownames(ydata.raw),]
dim(xdata.raw_1) # 1080 20501
# Order ydata the same as assay
ydata.raw <- ydata.raw[TCGAbarcode(rownames(xdata.raw_1)), ]
xdata <- xdata.raw_1
ydata <- ydata.raw %>% select(status)
# ydata <- ydata.raw %>% select(time, status)
data <- as.matrix(cbind(ydata, xdata))
rownames(data) <- rownames(xdata)
dim(data) # 1080 20221
data1 <- t(data)
write.table(data1,"TCGA_pro_outcome.txt",quote=F,sep="\t")
# Perform DEGs according to survival status and normalized -------------------------------------------
data_outcome <- read.table("TCGA_pro_outcome.txt",header=T,sep='\t', check.names = F)
dim(data_outcome) # 20221 1080
data_outcome[2,1]
## Express data
xdatat <- data_outcome[-1,]
dim(xdatat) # 20220 1080
## State of existence
ydata <- t(data_outcome[1,])
sum(ydata[,1]) # 152 dead
## Label grouping
ydata[which(ydata[,1] == "1"), 1] <- c("Dead")
ydata[which(ydata[,1] == "0"), 1] <- c("Alive")
# DESeq2 package to do DEGs of RNA-seq data -------------------------------------------------
exprSet <- round(xdatat)
dim(exprSet) # 20220 1080
group_list <- as.factor(ydata)
colData <- data.frame(row.names=colnames(exprSet), group_list=group_list)
colnames(colData) <- c("outcome")
dim(colData)
library(DESeq2)
library(limma)
library(pasilla)
dds <- DESeqDataSetFromMatrix(countData = exprSet,
colData = colData,
design = ~ outcome)
dds2 <- DESeq(dds)
resultsNames(dds2)
# res <- results(dds2, contrast=c("outcome","Dead","Alive"))
# summary(res)
# res_order <- res[order(res$padj),]
# res_order <- as.data.frame(res_order)
# write.csv(res_order,file= "DEG_res_order_DA.csv")
res1 <- results(dds2, alpha = 0.01)
write.csv(res1,file= "DEG_res.csv")
diff_gene_deseq2 <- subset(res1, padj < 0.01)
# diff_gene_deseq2 <- subset(res1, padj < 0.05 & abs(log2FoldChange) > 0.5849625)
diff_gene_deseq2 <- as.data.frame(diff_gene_deseq2)
dim(diff_gene_deseq2)
write.csv(diff_gene_deseq2,file= "DEG_Dead_vs_Alive_p001.csv")
# Data normalization -------------------------------------------------------------------
vst_dat <- vst(dds2, blind = TRUE)
dat111 <- assay(vst_dat)
dim(dat111) # 20220 1080
data_norm_clin <- t(dat111)
dim(data_norm_clin) # 1080 20220
# 带有生存时间的 -----------------------------------------------------------------
ydata_clin <- ydata.raw %>% select(time, status)
data_clin <- as.matrix(cbind(ydata_clin, data_norm_clin))
rownames(data_clin) <- rownames(data_norm_clin)
dim(data_clin) # 1080 20222
data_clin1 <- t(data_clin)
write.table(data_clin1,"TCGA_pro_norm_clin.txt",quote=F,sep="\t")