R语言中这个筛选差异基因的方式可读性很好，值得推荐

2022-03-27 09:56:50 浏览数 (2)
代码语言：javascript复制
text <- "Gene    log2FoldChange    padj
a    1    0.1
b    2    0.05
d    3    0.001
e    -2    0.0002
G    0    0.0001"

res_output <- read.table(text=text, row.names=NULL, header=T)

padj_thresh = 0.05
log2FC = 1
groupA = 'trt'
groupB = 'untrt'

ifelse 标记差异基因

不想安装额外的包，可以用 ifelse，要稍微复杂一点。

代码语言：javascript复制
res_output$level <- ifelse(res_output$padj<=padj_thresh,
                      ifelse(res_output$log2FoldChange>=log2FC,
                             paste(groupA,"UP"),
                             ifelse(res_output$log2FoldChange<=(-1)*(log2FC),
                                    paste(groupB,"UP"), "NoDiff")) , "NoDiff")

或

代码语言：javascript复制
res_output$level <-
ifelse(res_output$padj<=padj_thresh & res_output$log2FoldChange>=log2FC, paste(groupA,"UP"),
ifelse(res_output$padj<=padj_thresh & res_output$log2FoldChange<=(-1)*(log2FC), paste(groupB,"UP"),
       "NoDiff"))

dplyr::case-when 标记差异基因

比之前简洁了一些，可读性强

代码语言：javascript复制
library(dplyr)
res_output %>% mutate(level = case_when(
  (padj<=padj_thresh) & (log2FoldChange>=log2FC) ~ paste(groupA,"UP"),
  (padj<=padj_thresh) & (log2FoldChange<=(-1)*log2FC) ~ paste(groupB, "UP"),
  TRUE ~ "NoDiff"
  ))

dplyr::if_else 会更快一点

速度快一点，但可读性弱了一些。

代码语言：javascript复制
res_output %>% mutate(level =
if_else(res_output$padj<=padj_thresh & res_output$log2FoldChange>=log2FC, paste(groupA,"UP"),
if_else(res_output$padj<=padj_thresh & res_output$log2FoldChange<=(-1)*(log2FC), paste(groupB,"UP"),
       "NoDiff")))

case-when只保留差异基因的名字

代码语言：javascript复制
library(dplyr)
res_output %>% mutate(diff_gene = case_when(
  (padj<=padj_thresh) & (log2FoldChange>=log2FC) ~ Gene,
  (padj<=padj_thresh) & (log2FoldChange<=(-1)*log2FC) ~ Gene,
  TRUE ~ ""
  ))

case-when每隔 1 个基因保留 1 个

临时生成列时操作起来更方便了

代码语言：javascript复制
library(dplyr)
res_output %>% mutate(rank=1:n(),
                       keep_gene = case_when(
  rank %% 2 == 1 ~ Gene,
  TRUE ~ ""
  ))

dplyr::if_else速度最快！

dplyr::if_else速度最快！

代码语言：javascript复制
suppressPackageStartupMessages(library(tidyverse))

microbenchmark::microbenchmark(
  case_when(1:1000 < 100 ~ "low", TRUE ~ "high"),
  if_else(1:1000 < 3, "low", "high"),
  ifelse(1:1000 < 3, "low", "high")
)
#> Unit: microseconds
#>                                            expr     min      lq     mean
#>  case_when(1:1000 < 100 ~ "low", TRUE ~ "high") 384.786 418.629 953.4921
#>              if_else(1:1000 < 3, "low", "high")  61.943  67.686 128.9811
#>               ifelse(1:1000 < 3, "low", "high") 256.797 264.796 391.7180
#>    median       uq       max neval
#>  631.9420 708.4480 33149.364   100
#>   90.0435 127.9885  2496.182   100
#>  327.9695 460.8810  2354.246   100

Ref: https://community.rstudio.com/t/case-when-why-not/2685/2

如果不想安装额外包，用ifelse；如果是单个条件，用dplyr::if_else；如果多个条件，用dplyr::case_when (更可读)

case dplyr

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