用R根据logFC和p值批量标注基因上下调的N种方法
情景：假如有下面这些基因
expr logFC p.value
gene1 2.4667984 -2.9302068 0.07878848
gene2 1.4482891 -2.9680565 0.04675735
gene3 0.2481085 0.1787332 0.01685758
gene4 0.4244537 -1.0029163 0.02281603
gene5 1.6186835 -1.8350010 0.07323936
gene6 3.3965326 -2.2189805 0.04056557想达到下面这种效果： p.value<0.05的前提下 logFC>1标记为上调，logFC<-1的标记为下调
expr logFC p.value regulation
gene1 2.4667984 -2.9302068 0.07878848 none
gene2 1.4482891 -2.9680565 0.04675735 down
gene3 0.2481085 0.1787332 0.01685758 none
gene4 0.4244537 -1.0029163 0.02281603 down
gene5 1.6186835 -1.8350010 0.07323936 none
gene6 3.3965326 -2.2189805 0.04056557 down下面是用R实现的几种方式：
目标：筛选差异基因，标注上调下调
p.value小于0.05,且logFC绝对值大于1的为DEG
先建立模拟数据
set.seed(1445)
df <- data.frame(expr = runif(100,0.01,5), logFC = runif(100,-3,3), p.value = runif(100,0,0.1))
rownames(df) <- paste0("gene",1:100)
head(df)
test_p <- df$p.value <= 0.05#p.value<0.05
test_up <- df$logFC >=1#上调
test_down <- df$logFC <=-1#下调第一种方法：逻辑判断转为数字1和0,然后赋值
添加列，下调的乘以10的原因属个人喜好，但我觉得很有用
library(dplyr)
df <- mutate(df, regulation=test_p+test_up+10*test_down, method1 = "")
table(df$regulation)
#重新赋值
df[df$regulation==2,"method1"] <- "up"
df[df$regulation==11,"method1"] <- "down"
df[df$regulation==0|df$regulation==1|df$regulation==10,"method1"] <- "none"第二种方法：逻辑判断转为数字1和0，然后用ifelse
df$method2 <- ifelse(df$regulation==2, "up",
ifelse(df$regulation==11, "down", "none"))
head(df)第三种方法：逻辑判断转为数字1和0，然后用查询表
lookup <- c("2"="up","11"="down","0"="none","1"="none","10"="none")
df$method3 <- lookup[as.character(df$regulation)]
head(df)第四种方法：逻辑判断转为数字1和0，然后用dplyr包的case_when
df$method4 <- case_when(df$regulation == 2 ~ "up",
df$regulation == 11 ~ "down",
!df$regulation==2 |!df$regulation==11 ~ "none")第五种方法：ifelse直接判断任何赋值
df$method5 <- ifelse(test_p & test_up, "up",
ifelse(test_p & test_down, "down","none"))第六种方法：dplyr的case_when
df$method6 <- case_when(test_p & test_up ~ "up",
test_p & test_down ~ "down",
!test_p|!(test_down|test_up) ~ "none")第七种方法：逻辑判断转为数字1和0，然后用函数和for循环来标记
先写函数
my_regulation <- function(x){
if(x==2){
print("up")
}else if(x==11){
print("down")
}else
print("none")
}
#循环
method7 <- vector("character", nrow(df))
for (i in 1:nrow(df)) {
method7[i] <- my_regulation(df$regulation[i])
i <- i+1
}
#赋值
df$method7 <- data.frame(method7)
head(df)第八种方法：直接用函数和for循环
先关于df的函数
my_regulation2 <- function(x){
if(df$p.value[x]<0.05 & df$logFC[x]>1){
print("up")
}else if(df$p.value[x]<0.05 & df$logFC[x]< -1){
print("down")
}else
print("none")
}
#循环
method8 <- vector("character",nrow(df))
for (i in 1:nrow(df)) {
method8[i] <- my_regulation2(i)
i <- i+1
}
df$method8 <- method8
tail(df)最终结果
> head(df)
expr logFC p.value regulation method1 method2 method3 method4 method5 method6 method7 method8
1 2.4667984 -2.9302068 0.07878848 10 none none none none none none none none
2 1.4482891 -2.9680565 0.04675735 11 down down down down down down down down
3 0.2481085 0.1787332 0.01685758 1 none none none none none none none none
4 0.4244537 -1.0029163 0.02281603 11 down down down down down down down down
5 1.6186835 -1.8350010 0.07323936 10 none none none none none none none none
6 3.3965326 -2.2189805 0.04056557 11 down down down down down down down down检查是不是每个方法结果一样
for (i in 1:7) {
mi <- paste0('method',i)
print(sum(df[,paste0('method',i)]==df[,paste0('method',i+1)]))
i <- i+1
}结果如下
[1] 100
[1] 100
[1] 100
[1] 100
[1] 100
[1] 100
[1] 100以上方法，第3种方法查询表和ifelse函数最方便快捷。
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