作者: | 来源:互联网 | 2023-08-28 12:38
富集分析结果可视化加载R包library(tidyverse)library(stringr)library(circlize)library(ComplexHeatma
富集分析结果可视化
加载R包
library(tidyverse)
library(stringr)
library(circlize)
library(ComplexHeatmap)
导入数据
load("data.RData")
获取连续型颜色代码
col_fun = colorRamp2(c(-5,0,5), c("blue","white","red"))
col_fun(seq(-5,5, by=2.5))
数据清洗
在这里只展示了自己感兴趣的基因,由于要根据FC值对基因进行颜色填充,因此通过上方的代码生成对应的16进制颜色,经过case_when将颜色与数据整合
df <- dd %>% as.data.frame() %>%
separate_rows(.,geneID,cOnvert=TRUE,sep="/") %>%
left_join(.,geneList %>% as.data.frame() %>% dplyr::rename(FC=".") %>%
rownames_to_column(var="geneID"),by="geneID") %>%
select(2,geneID,Count,FC) %>%
filter(Description %in% c("Photosynthesis",
"Photosynthesis - antenna proteins","Fatty acid metabolism")) %>%
mutate(col=