适合用于FPKM数据求差异基因的ballgown算法

用户1359560

发布于 2021-12-06 10:12:14

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代码可运行

对于FPKM表达数据时，Edger，limma，和deseq等算法并不合适。而ballgown是针对于FPKM数据开发的差异基因算法，可以尝试。示例数据如下所示：

# --------------------------------------------------------
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setwd("D:\\SCIwork\\F5\\DEG")


library(tidyr)
library('ballgown')
load("mRNA_exprSet.Rda")

mRNA_exprSet[1:4,1:4]

# --------------------------------------------------------
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# 
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mRNA_exprSet <- mRNA_exprSet %>%
  tidyr::separate(gene_id, c("gene_name",
                             "gene_id",
                             "gene_biotype"), 
                  sep = " \\| ")

mRNA_exprSet <- mRNA_exprSet[,-(2:3)]
index <- duplicated(mRNA_exprSet$gene_name)
mRNA.data <- mRNA_exprSet[!index,]
dim(mRNA.data)


# --------------------------------------------------------
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#包含基因名的第一列转为行名
BLCA_fpkm_data = mRNA.data
rownames(BLCA_fpkm_data) = BLCA_fpkm_data[,1]
BLCA_fpkm_data  =  BLCA_fpkm_data[c(-1)]

#生成分组文件
load("mRNA_exprSet.Rda")
metadata <- data.frame(names(mRNA_exprSet)[-1])
for (i in 1:length(metadata[,1])) {
  num <- as.numeric(substring(metadata[i,1],14,15))
  if (num %in% seq(1,9)) {metadata[i,2] <- "T"}
  if (num %in% seq(10,29)) {metadata[i,2] <- "N"}
}

names(metadata) <- c("TCGA_id","group")

metadata$group <- as.factor(metadata$group)


# --------------------------------------------------------
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result_diff = stattest(gowntable = BLCA_fpkm_data ,
                       pData = metadata , 
                       covariate = "group" , 
                       getFC = TRUE , 
                       log =TRUE,
                       meas='FPKM',
                       feature="gene")


result_diff$LogFC <- log2(result_diff$fc)

result_diff$LogFC_abs <- abs(result_diff$LogFC)

write.csv (result_diff, "mRNA_BLCA_fpkm_diff.csv", row.names = F)

# --------------------------------------------------------
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foldChange =0.5

padj=0.05

diffSig=result_diff[which(result_diff$pval< padj & result_diff$LogFC_abs > foldChange),]

dim(diffSig)

write.csv(diffSig, file="diffSig_mRNA_BLCA.csv")