tcga联合gtex数据去除批次效应后差异分析代码
时间: 2024-02-12 21:07:56 浏览: 134
这里提供一份基于R语言的TCGA联合GTEx数据去除批次效应后的差异分析代码,供您参考:
```R
# 安装所需的包
install.packages("edgeR")
install.packages("limma")
install.packages("ggplot2")
install.packages("dplyr")
install.packages("tidyr")
install.packages("ComBat")
# 导入TCGA和GTEx的RNA-seq原始数据并进行质量控制和基因表达量计算
library(edgeR)
library(limma)
library(ComBat)
# 导入TCGA和GTEx的数据,注意文件格式和路径
tcga_data <- read.table("tcga_data.txt", header = T, row.names = 1, sep = "\t")
gtex_data <- read.table("gtex_data.txt", header = T, row.names = 1, sep = "\t")
# 将TCGA和GTEx的数据合并
all_data <- cbind(tcga_data, gtex_data)
# 进行基因表达量计算
all_counts <- apply(all_data, 1, sum)
all_tpm <- sweep(all_data, 2, all_counts, FUN = "/") * 10^6
# 进行批次效应去除
batch <- c(rep("TCGA", ncol(tcga_data)), rep("GTEx", ncol(gtex_data)))
batch_combat <- ComBat(dat = all_tpm, batch = batch, mod = NULL, par.prior = TRUE, prior.plots = FALSE)
# 进行差异分析
counts <- t(batch_combat$dat)
group <- c(rep("TCGA", ncol(tcga_data)), rep("GTEx", ncol(gtex_data)))
design <- model.matrix(~0+group)
colnames(design) <- levels(group)
y <- DGEList(counts = counts, group = group)
y <- calcNormFactors(y, method = "TMM")
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design)
qlf <- glmQLFTest(fit, coef = 1)
# 根据FDR筛选差异表达基因
diff_genes <- topTags(qlf, n = Inf, sort.by = "none")$table
diff_genes <- diff_genes[diff_genes$FDR < 0.05,]
# 对差异表达基因进行注释和功能分析
library(dplyr)
library(tidyr)
# 可以根据需要选择不同的基因注释数据库
# 这里以ENSEMBL为例,需要提前下载ENSEMBL注释文件
anno_file <- "Homo_sapiens.GRCh38.98.gtf.gz"
gene_anno <- read.table(gzfile(anno_file), header = F, stringsAsFactors = F)
gene_anno <- gene_anno[gene_anno$V3 == "gene",]
gene_anno$gene_id <- gsub("\"", "", sapply(strsplit(gene_anno$V9, split = ";"), function(x) x[1]))
gene_anno$gene_name <- gsub("\"", "", sapply(strsplit(gene_anno$V9, split = ";"), function(x) x[5]))
diff_genes_anno <- diff_genes %>%
left_join(gene_anno, by = c("GeneID" = "gene_id")) %>%
select("GeneID", "logFC", "FDR", "gene_name")
# 对差异表达基因进行富集分析
library(clusterProfiler)
# 选择需要分析的物种和基因注释数据库
species <- "Homo sapiens"
org <- "org.Hs.eg.db"
enrich_res <- enrichGO(diff_genes_anno$gene_name,
OrgDb = org,
keyType = "SYMBOL",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.05,
universe = unique(gene_anno$gene_name))
# 将结果可视化展示
library(ggplot2)
enrich_res %>%
mutate(Term = fct_reorder(Term, -log10(pvalue))) %>%
ggplot(aes(x = -log10(pvalue), y = as.factor(Term))) +
geom_point(size = 3) +
scale_y_discrete(limits = rev(levels(enrich_res$Term))) +
labs(x = "-log10(pvalue)", y = "GO Term") +
ggtitle("GO Enrichment Analysis of DE Genes") +
theme_bw(base_size = 15)
```
需要注意的是,这段代码中涉及到的数据文件格式和路径需要根据实际情况进行修改。此外,在进行差异分析和富集分析时,需要选择合适的基因注释数据库和分析参数。