GEO数据挖掘基本流程与代码
5个月前 (12-07)
0 点赞
0 收藏
0 评论
15 已阅读
写在前面：本文内容出自生信技能树的生信入门系列课程笔记，感谢小洁老师、Jimmy老师的分享。
GEO数据挖掘分析思路：
1.找数据，找到GSE编号
2.下载数据（表达矩阵、临床信息、分组信息）
3.数据探索（分组之间是否有差异，PCA、整个数据的热图）
4.limma差异分析及可视化（P值、logFC，火山图、差异基因的热图）
5.富集分析KEGG、GO
注意：该标准流程只适用于表达芯片分析，甲基化、SNP等芯片的流程详见生信技能树专题。
GEO表达芯片分析的要点:
解决probe_id与gene symbol、样本编号GSM与分组之间的对应关系。
GO富集的3个方面：
1.分子功能（Molecular Function，MF ）
2.细胞组分（Cellular Component ，CC）
3.生物过程（Biological Process ，BP）
GO富集的意义：
通过生物属性的从属关系，达到比通路富集更深层次的挖掘。比如差异基因都富集到了某些通路上，而这些通路的相关性并不强烈，但是GO富集通过它们从属关系，可以发现它们都指向或从属于某个更深层次的通路。
不同GSE合并要处理批次效应。
以下是步骤及对应代码，每一个大标题代表实战中的一个脚本文件，长脚本管理也可参照该方式：
00_pre_install.R
options("repos"="https://mirrors.ustc.edu.cn/CRAN/")
if(!require("BiocManager")) install.packages("BiocManager",update = F,ask = F)
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
cran_packages <- c('tidyr',
'tibble',
'dplyr',
'stringr',
'ggplot2',
'ggpubr',
'factoextra',
'FactoMineR',
'devtools',
'cowplot',
'patchwork',
'basetheme',
'ggthemes',
'paletteer',
'AnnoProbe',
'tinyarray')
Biocductor_packages <- c('GEOquery',
'hgu133plus2.db',
'ggnewscale',
"limma",
"impute",
"GSEABase",
"GSVA",
"clusterProfiler",
"org.Hs.eg.db",
"preprocessCore",
"enrichplot",
"ggplotify")
for (pkg in cran_packages){
if (! require(pkg,character.only=T) ) {
install.packages(pkg,ask = F,update = F)
require(pkg,character.only=T)
}
}
for (pkg in Biocductor_packages){
if (! require(pkg,character.only=T) ) {
BiocManager::install(pkg,ask = F,update = F)
require(pkg,character.only=T)
}
}
for (pkg in c(Biocductor_packages,cran_packages)){
require(pkg,character.only=T)
#character.only=T指的是require函数加载的是pkg所代表的字符串表示的包，而不是pkg这个包
01_start_GEO.R
rm(list = ls())
library(GEOquery)
gse_number = "GSE56649"
eSet <- getGEO(gse_number,
destdir = '.',
getGPL = F)
class(eSet)
length(eSet)
eSet = eSet[[1]]
#1.提取表达矩阵exp
exp <- exprs(eSet)
exp[1:4,1:4]
exp = log2(exp+1) #看数据是否已经取过log值——数值是否跨数量级
boxplot(exp) #看一下数据是否基本在一条直线上
#若数据差异较大，则需标准化一下：exp2=limma::normalizeBetweenArrays(exp)
#(2)提取临床信息
pd <- pData(eSet)
#(3)调整pd的行名顺序与exp列名完全一致
p = identical(rownames(pd),colnames(exp));p
if(!p) exp = exp[,match(rownames(pd),colnames(exp))]
#(4)提取芯片平台编号
gpl_number <- eSet@annotation
save(gse_number,pd,exp,gpl_number,file = "step1output.Rdata")
02_group_ids.R
# Group(实验分组)和ids(探针注释)
rm(list = ls())
load(file = "step1output.Rdata")
library(stringr)
# 1.Group----实验分组要去阅读临床信息的表格来获取，每个GSE的都不一样
# 第一类，有现成的可以用来分组的列
Group = pd$`disease state:ch1`
#第二类，自己生成
Group = c(rep("RA",times=13),
rep("control",times=9))
Group = rep(c("RA","control"),times = c(13,9))
#第三类，匹配关键词，自行分类
Group=ifelse(str_detect(pd$source_name_ch1,"control"),
"control",
"RA")
#设置参考水平，指定levels，对照组在前，处理组在后
Group = factor(Group,
levels = c("control","RA"))
Group
#2.探针注释的获取-----------------
#捷径
find_anno(gpl_number)
ids <- AnnoProbe::idmap('GPL570')
#方法1 BioconductorR包(最常用)
gpl_number
#http://www.bio-info-trainee.com/1399.html
if(!require(hgu133plus2.db))BiocManager::install("hgu133plus2.db")
library(hgu133plus2.db)
ls("package:hgu133plus2.db")
#用toTable把内置数据转换为数据框
ids <- toTable(hgu133plus2SYMBOL)
head(ids)
# 方法2 读取GPL平台的soft文件，按列取子集
##https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL570
if(F){
#注：soft文件列名不统一，活学活用，有的表格里没有symbol列，也有的GPL平台没有提供注释
a = getGEO(gpl_number,destdir = ".")
b = a@dataTable@table
colnames(b)
ids2 = b[,c("ID","Gene Symbol")]
colnames(ids2) = c("probe_id","symbol")
ids2 = ids2[ids2$symbol!="" & !str_detect(ids2$symbol,"///"),]
}
# 方法3 官网下载注释文件并读取
##http://www.affymetrix.com/support/technical/byproduct.affx?product=hg-u133-plus
# 方法4 自主注释
#https://mp.weixin.qq.com/s/mrtjpN8yDKUdCSvSUuUwcA
save(exp,Group,ids,gse_number,file = "step2output.Rdata")
03_pca_heatmap.R
rm(list = ls())
load(file = "step1output.Rdata")
load(file = "step2output.Rdata")
#输入数据：exp和Group
#Principal Component Analysis
#http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials
# 1.PCA 图----
dat=as.data.frame(t(exp))
library(FactoMineR)
library(factoextra)
dat.pca <- PCA(dat, graph = FALSE)
pca_plot <- fviz_pca_ind(dat.pca,
geom.ind = "point", # show points only (nbut not "text")
col.ind = Group, # color by groups
palette = c("#00AFBB", "#E7B800"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Groups"
)
pca_plot
ggsave(plot = pca_plot,filename = paste0(gse_number,"_PCA.png"))
save(pca_plot,file = "pca_plot.Rdata")
# 2.top 1000 sd 热图----
#挑出表达矩阵里方差最大的1000个探针的名字！
cg=names(tail(sort(apply(exp,1,sd)),1000))
n=exp[cg,]
# 直接画热图，对比不鲜明——因为有些热图颜色对应的取值范围默认是从数据最小值到最大值，离群值会造成大部分数据的热图颜色相近
library(pheatmap)
annotation_col=data.frame(group=Group)
rownames(annotation_col)=colnames(n)
pheatmap(n,
show_colnames =F,
show_rownames = F,
#cluster_cols = F, #不按分组聚类
annotation_col=annotation_col
)
# 按行标准化
pheatmap(n,
show_colnames =F,
show_rownames = F,
annotation_col=annotation_col,
scale = "row", #标准化的依据
breaks = seq(-3,3,length.out = 100) #颜色分割
)
dev.off()
04_DEG.R
rm(list = ls())
load(file = "step2output.Rdata")
#差异分析，用limma包来做
#需要表达矩阵和Group，不需要改
library(limma)
design=model.matrix(~Group)
fit=lmFit(exp,design)
fit=eBayes(fit)
deg=topTable(fit,coef=2,number = Inf) #以上操作为用limma包对表达矩阵进行一系列基于统计学原理的处理分析。
#为deg数据框添加几列
#1.加probe_id列，把行名变成一列
library(dplyr)
deg <- mutate(deg,probe_id=rownames(deg))
head(deg)
#2.加上探针注释
ids = ids[!duplicated(ids$symbol),]
deg <- inner_join(deg,ids,by="probe_id") #随机去掉对应同一基因的多个探针，只留下一个探针
head(deg)
nrow(deg)
#3.加change列,标记上下调基因
logFC_t=1
P.Value_t = 0.05 #logFC和p-value可以自己定义
k1 = (deg$P.Value < P.Value_t)&(deg$logFC < -logFC_t)
k2 = (deg$P.Value < P.Value_t)&(deg$logFC > logFC_t)
deg <- mutate(deg,change = ifelse(k1,"down",ifelse(k2,"up","stable")))
table(deg$change)
#4.加ENTREZID列，用于富集分析（symbol转entrezid，然后inner_join）
library(clusterProfiler)
library(org.Hs.eg.db)
s2e <- bitr(deg$symbol,
fromType = "SYMBOL",
toType = "ENTREZID",
OrgDb = org.Hs.eg.db)#人类,不同物种的OrgDb不同！！！
#其他物种http://bioconductor.org/packages/release/BiocViews.html#___OrgDb
dim(deg)
deg <- inner_join(deg,s2e,by=c("symbol"="SYMBOL"))
dim(deg)
length(unique(deg$symbol))
save(Group,deg,logFC_t,P.Value_t,gse_number,file = "step4output.Rdata")
05_plots.R
rm(list = ls())
load(file = "step1output.Rdata")
load(file = "step4output.Rdata")
#1.火山图----
library(dplyr)
library(ggplot2)
dat = deg[!duplicated(deg$symbol),] #去掉一个探针对应多个gene symbol的情况
p <- ggplot(data = dat,
aes(x = logFC,
y = -log10(P.Value))) +
geom_point(alpha=0.4, size=3.5,
aes(color=change)) +
ylab("-log10(Pvalue)")+
scale_color_manual(values=c("blue", "grey","red"))+
geom_vline(xintercept=c(-logFC_t,logFC_t),lty=4,col="black",lwd=0.8) +
geom_hline(yintercept = -log10(P.Value_t),lty=4,col="black",lwd=0.8) +
theme_bw()
p
for_label <- dat%>%
filter(symbol %in% c("HADHA","LRRFIP1"))
volcano_plot <- p +
geom_point(size = 3, shape = 1, data = for_label) +
ggrepel::geom_label_repel(
aes(label = symbol),
data = for_label,
color="black"
)
volcano_plot
ggsave(plot = volcano_plot,filename = paste0(gse_number,"_volcano.png"))
#2.差异基因热图----
load(file = 'step2output.Rdata')
# 表达矩阵行名替换
exp = exp[dat$probe_id,]
rownames(exp) = dat$symbol
if(F){
#全部差异基因
cg = dat$symbol[dat$change !="stable"]
length(cg)
}else{
#取前10上调和前10下调
x=dat$logFC[dat$change !="stable"]
names(x)=dat$symbol[dat$change !="stable"]
cg=names(c(head(sort(x),10),tail(sort(x),10)))
length(cg)
}
n=exp[cg,]
dim(n)
#差异基因热图
library(pheatmap)
annotation_col=data.frame(group=Group)
rownames(annotation_col)=colnames(n)
heatmap_plot <- pheatmap(n,show_colnames =F,
scale = "row",
#cluster_cols = F,
annotation_col=annotation_col,
breaks = seq(-3,3,length.out = 100)
)
heatmap_plot
ggsave(heatmap_plot,filename = paste0(gse_number,"_heatmap.png"))
# 3.感兴趣基因的箱线图----
g = c(head(cg,3),tail(cg,3))
library(tidyr)
library(tibble)
library(dplyr)
dat = t(exp[g,]) %>%
as.data.frame() %>%
rownames_to_column() %>%
mutate(group = Group)
pdat = dat%>%
pivot_longer(cols = 2:(ncol(dat)-1),
names_to = "gene",
values_to = "count")
pdat$gene = factor(pdat$gene,levels = cg,ordered = T)
pdat$change = ifelse(pdat$gene %in% head(cg,10),"down","up")
library(ggplot2)
library(paletteer)
box_plot = ggplot(pdat,aes(gene,count))+
geom_boxplot(aes(fill = group))+
#scale_fill_manual(values = c("blue","red"))+
scale_fill_paletteer_d("basetheme::minimal")+
geom_jitter()+
theme_bw()+
facet_wrap(~change,scales = "free")
box_plot
ggsave(box_plot,filename = paste0(gse_number,"_boxplot.png"))
# 4.感兴趣基因的相关性----
library(corrplot)
M = cor(t(exp[g,]))
pheatmap(M)
my_color = rev(paletteer_d("RColorBrewer::RdYlBu"))
my_color = colorRampPalette(my_color)(10)
corrplot(M, type="upper",
method="pie",
order="hclust",
col=my_color,
tl.col="black",
tl.srt=45)
library(cowplot)
cor_plot <- recordPlot()
# 拼图
load("pca_plot.Rdata")
library(patchwork)
library(ggplotify)
(pca_plot + volcano_plot +as.ggplot(heatmap_plot))/box_plot
plot_grid(cor_plot,heatmap_plot$gtable)
06_anno.R
rm(list = ls())
load(file = 'step4output.Rdata')
library(clusterProfiler)
library(ggthemes)
library(org.Hs.eg.db)
library(dplyr)
library(ggplot2)
library(stringr)
library(enrichplot)
# 1.GO 富集分析----
#(1)输入数据
gene_up = deg$ENTREZID[deg$change == 'up']
gene_down = deg$ENTREZID[deg$change == 'down']
gene_diff = c(gene_up,gene_down)
#(2)富集
#以下步骤耗时很长，设置了存在即跳过
if(!file.exists(paste0(gse_number,"_GO.Rdata"))){
ego <- enrichGO(gene = gene_diff,
OrgDb= org.Hs.eg.db,
ont = "ALL",
readable = TRUE)
ego_BP <- enrichGO(gene = gene_diff,
OrgDb= org.Hs.eg.db,
ont = "BP",
readable = TRUE)
#ont参数：One of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
save(ego,ego_BP,file = paste0(gse_number,"_GO.Rdata"))
}
load(paste0(gse_number,"_GO.Rdata"))
#(3)可视化
#条带图
barplot(ego)
#气泡图
dotplot(ego)
dotplot(ego, split = "ONTOLOGY", font.size = 10,
showCategory = 5) +
facet_grid(ONTOLOGY ~ ., space = "free_y",scales = "free_y") +
scale_y_discrete(labels = function(x) str_wrap(x, width = 45))
#geneList 用于设置下面图的颜色
geneList = deg$logFC
names(geneList)=deg$ENTREZID
#(3)展示top通路的共同基因，要放大看。
#Gene-Concept Network
cnetplot(ego,categorySize="pvalue", foldChange=geneList,colorEdge = TRUE)
cnetplot(ego, showCategory = 3,foldChange=geneList, circular = TRUE, colorEdge = TRUE)
#Enrichment Map,这个函数最近更新过，版本不同代码会不同
Biobase::package.version("enrichplot")
if(T){
emapplot(pairwise_termsim(ego)) #新版本
}else{
emapplot(ego)#旧版本
}
#(4)展示通路关系 https://zhuanlan.zhihu.com/p/99789859
#goplot(ego)
goplot(ego_BP)
#(5)Heatmap-like functional classification
heatplot(ego,foldChange = geneList,showCategory = 8)
# 2.KEGG pathway analysis----
#上调、下调、差异、所有基因
#（1）输入数据
gene_up = deg[deg$change == 'up','ENTREZID']
gene_down = deg[deg$change == 'down','ENTREZID']
gene_diff = c(gene_up,gene_down)
#（2）对上调/下调/所有差异基因进行富集分析
if(!file.exists(paste0(gse_number,"_KEGG.Rdata"))){
kk.up <- enrichKEGG(gene = gene_up,
organism = 'hsa')
kk.down <- enrichKEGG(gene = gene_down,
organism = 'hsa')
kk.diff <- enrichKEGG(gene = gene_diff,
organism = 'hsa')
save(kk.diff,kk.down,kk.up,file = paste0(gse_number,"_KEGG.Rdata"))
}
load(paste0(gse_number,"_KEGG.Rdata"))
#(3)看看富集到了吗？https://mp.weixin.qq.com/s/NglawJgVgrMJ0QfD-YRBQg
table(kk.diff@result$p.adjust<0.05)
table(kk.up@result$p.adjust<0.05)
table(kk.down@result$p.adjust<0.05)
#(4)双向图
# 富集分析所有图表默认都是用p.adjust,富集不到可以退而求其次用p值，在文中说明即可
down_kegg <- kk.down@result %>%
filter(pvalue<0.05) %>% #筛选行
mutate(group=-1) #新增列
up_kegg <- kk.up@result %>%
filter(pvalue<0.05) %>%
mutate(group=1)
source("kegg_plot_function.R") #加载某个函数
g_kegg <- kegg_plot(up_kegg,down_kegg)
g_kegg
#g_kegg +scale_y_continuous(labels = c(2,0,2,4,6))
ggsave(g_kegg,filename = 'kegg_up_down.png')
# 3.GSEA作kegg和GO富集分析----
#https://www.yuque.com/xiaojiewanglezenmofenshen/dbwkg1/ytawgg
#(1)查看示例数据
data(geneList, package="DOSE")
#(2)将我们的数据转换成示例数据的格式
geneList=deg$logFC
names(geneList)=deg$ENTREZID
geneList=sort(geneList,decreasing = T)
#(3)GSEA富集
kk_gse <- gseKEGG(geneList = geneList,
organism = 'hsa',
verbose = FALSE)
down_kegg<-kk_gse[kk_gse$pvalue<0.05 & kk_gse$enrichmentScore < 0,];down_kegg$group=-1
up_kegg<-kk_gse[kk_gse$pvalue<0.05 & kk_gse$enrichmentScore > 0,];up_kegg$group=1
#(4)可视化
g2 = kegg_plot(up_kegg,down_kegg)
g2
07_string.R
ppi网络分析通过string网站和Cytoscape完成，这里显示如何用代码生成输入数据。
rm(list = ls())
load("step4output.Rdata")
gene_up= deg[deg$change == 'up','symbol']
gene_down=deg[deg$change == 'down','symbol']
gene_diff = c(gene_up,gene_down)
# 1.制作string的输入数据
write.table(gene_diff,
file="diffgene.txt",
row.names = F,
col.names = F,
quote = F)
# 从string网页获得string_interactions.tsv
# 2.准备cytoscape的输入文件
p = deg[deg$change != "stable",
c("symbol","logFC")]
head(p)
write.table(p,
file = "deg.txt",
sep = "\t",
quote = F,
row.names = F)
# string_interactions.tsv是网络文件
# deg.txt是属性表格
一些相关学习资料
GSEA：
https://www.yuque.com/docs/share/a67a180f-dd2b-4f6f-96c2-68a4b86fe862?#
富集分析：
http://yulab-smu.top/clusterProfiler-book/index.html
弦图：https://www.yuque.com/xiaojiewanglezenmofenshen/dbwkg1/dgs65p
GOplot：
https://mp.weixin.qq.com/s/LonwdDhDn8iFUfxqSJ2Wew
还没有任何评论，你来说两句吧