The goal of enrichR is to for enrich analysis and visual the data.
if(!require(ggplot2))install.packages("ggplot2")
if(!require(tidyverse))install.packages("tidyverse")
if(!require(ggprism))remotes::install_github("csdaw/ggprism")
if(!require(devtools))install.packages("devtools")
if(!require(ggvenn))devtools::install_github("yanlinlin82/ggvenn")
if(!require(UpSetR))install.packages("UpSetR")
if(!require(clusterProfiler))install.packages("clusterProfiler")
if(!require(ggord))install_github('fawda123/ggord')
if(!require(grid))remotes::install_github("thomasp85/grid")
if(!require(ggnewscale))devtools::install_github("eliocamp/ggnewscale")You can install the released version of enrichR from local with:
install.packages("enrichR_0.0.2.0.tar.gz", repos = NULL, type = "source")And the development version from GitHub with:
install.packages("devtools")
devtools::install_github("chaimol/enrichR")Usage.pdf
This is basic example
library(enrichR)
### PCA
read_counts <- read.delim("data/all_raw_counts.txt",sep="\t",header = TRUE,row.names = 1)
group_level <- substr(colnames(read_counts),1,2)
get_pca(read_counts, group_level,groupname="WTvsMT")
###Venn图（Venn图只能输出2-4组的维恩图，大于4则只输出upset图）
venn <- read.csv("data/venn.csv",header = T)
gene2venn(venn[1:3],"demo3")
gene2venn(venn[1:4],"demo4")
gene2venn(venn[1:2],"demo2")
###GO,KEGG,TF enrich amalysis
#每一列都可以单独进行各种富集分析
#参数1是基因列，参数2是输出的文件前缀名，参数3是对应的数据库，需要自行下载对应物种的数据库
#GO,KEGG,TF数据格式参考 data目录里的格式
go1 <- GOenrich(venn$kang5,"kang5",GOfile = "data/GO.info")
kegg1 <- KEGGenrich(venn$gan8,"gan8","data/KEGG.info")
tf1 <- TFenrich(venn$kang2,"kang2","data/TF.info")
#参数1：基因列表
#参数2：输出文件名前缀
#参数3：富集种类的数据库文件(要求必须使用tab分隔符)
#GO.info 需要4列
#KEGG.info 需要3列
#TF.info 需要3列或4列（只使用前三列）
###dot plot
load("data/data_GO_all.Rdata")
dotplot(data_GO_all,xlabels=xlabels,output="All_GO")
dotplot(data_GO_all,xlabels=xlabels,output="All_GO",range=c(3,5))