ixxmu
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2 months ago 送你一个环形富集分析图 by 老俊俊的生信笔记
引言
❝好久没用 circlize 了，都忘记怎么用了。今天画个环形富集分析图。网上教程也多，开动自己的脑筋和创意画一个。
富集分析
首先获得富集结果：
library(ggplot2)
library(org.Hs.eg.db)
library(clusterProfiler)
library(dplyr)
# load test data
data(geneList, package="DOSE")
# check
head(geneList)
# 4312 8318 10874 55143 55388 991
# 4.572613 4.514594 4.418218 4.144075 3.876258 3.677857
# enrichment for control
ego1 <- enrichGO(gene = names(geneList)[1:500],
OrgDb = org.Hs.eg.db,
keyType = "ENTREZID",
ont = "ALL",
qvalueCutoff = 1,
pvalueCutoff = 1,
readable = T)
# get top 6 terms for visualization
ego1_df <- data.frame(ego1) %>%
group_by(ONTOLOGY) %>%
arrange(pvalue) %>%
slice_head(n = 6) %>%
rowwise() %>%
mutate(fc = eval(parse(text = GeneRatio))/eval(parse(text = BgRatio)))
画图
就拿上面的结果来绘图：
# plot
library(circlize)
# colors for ONTOLOGY group
col <- rep(rand_color(n = length(unique(ego1_df$ONTOLOGY))),
table(ego1_df$ONTOLOGY))
circos.clear()
circos.initialize(sectors = ego1_df$ID,xlim = c(0,1))
# first GO id
circos.track(sectors = ego1_df$ID,ylim = c(0,1),
bg.col = col,
panel.fun = function(x, y){
circos.text(x = CELL_META$xcenter,y = CELL_META$ycenter,
labels = CELL_META$sector.index,
cex = 0.75)
})
# add count track
circos.track(sectors = ego1_df$ID,ylim = c(0,1),track.height = 0.1,
panel.fun = function(x, y){
# circos.axis(h = "bottom")
})
for (i in 1:nrow(ego1_df)){
circos.rect(xleft = 0,xright = ego1_df$Count[i]/max(ego1_df$Count),
ybottom = 0.25,ytop = 0.75,
sector.index = ego1_df$ID[i],
# track.index = 1,
col = "#9933CC")
# add xaxis
circos.axis(h = "bottom",major.at = c(0,1),labels = c(0,max(ego1_df$Count)),
sector.index = ego1_df$ID[i])
}
# foldchange enriment
circos.track(sectors = ego1_df$ID,ylim = c(0,1),track.height = 0.05)
for (i in 1:nrow(ego1_df)){
circos.text(x = 0.5,y = 0.5,
labels = paste("FC:",round(ego1_df$fc[i],digits = 1),sep = " "),
sector.index = ego1_df$ID[i])
}
circos.track(sectors = ego1_df$ID,ylim = c(0,ceiling(max(ego1_df$fc))))
for (i in 1:nrow(ego1_df)){
circos.barplot(value = ego1_df$fc[i],pos = 0.5,
sector.index = ego1_df$ID[i],
# track.index = 4,
col = "#FF6666")
}
# -log10 pvalue
circos.track(sectors = ego1_df$ID,ylim = c(0,ceiling(max(-log10(ego1_df$pvalue)))))
for (i in 1:nrow(ego1_df)){
circos.barplot(value = -log10(ego1_df$pvalue)[i],pos = 0.5,
sector.index = ego1_df$ID[i],
# track.index = 2,
col = col[i])
}
❝包括了 count，富集倍数，p 值信息。当然你也可以继续添加其它信息上去。
结尾
❝路漫漫其修远兮,吾将上下而求索。
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知识星球：
❝enrichCluster 关于非模式物种富集分析的使用 tidyCoverage 计算 bigwig 文件在目标区域的信号 桑基图加富集图一行代码出图? GEO 上传数据最新教程 试试 bulkRNA 做拟时序分析？ ClusterGVis 绘制 lineplot 的优化 RNAseqQC 给你的数据来个全面的 QC 检查 如何 Pull Request 到 github 贡献你的代码 ggplot 添加分类型数据双坐标轴 富集分析流星图?
https://mp.weixin.qq.com/s/sC-srCJhvjbdMFIXk6knJQ