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