{"id":26915,"date":"2024-01-29T17:30:35","date_gmt":"2024-01-29T09:30:35","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26915"},"modified":"2024-01-29T17:30:36","modified_gmt":"2024-01-29T09:30:36","slug":"%e8%bf%98%e5%9c%a8%e4%b8%ba%e5%8d%95%e5%9f%ba%e5%9b%a0gsea%e5%8f%91%e6%84%81%ef%bc%9f%e7%9c%8b%e8%bf%99%e9%87%8c%ef%bc%81%e5%b0%8f%e6%9e%9c%e6%95%99%e4%bd%a0%e5%a6%82%e4%bd%95%e6%8e%8c%e6%8f%a1","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26915","title":{"rendered":"\u8fd8\u5728\u4e3a\u5355\u57fa\u56e0GSEA\u53d1\u6101\uff1f\u770b\u8fd9\u91cc\uff01\u5c0f\u679c\u6559\u4f60\u5982\u4f55\u638c\u63e1\u5355\u57fa\u56e0GSEA\u901a\u8def\u5bcc\u96c6\uff01!"},"content":{"rendered":"

\u5c0f\u4f19\u4f34\u5728\u505a\u751f\u4fe1\u5206\u6790\u65f6\u5019\uff0c\u7ecf\u5e38\u4f7f\u7528\u7684\u6d41\u7a0b\u5c31\u662f\u7b5b\u9009\u57fa\u56e0\u4e86\uff0c\u800c\u5230\u4e86\u6700\u540e\u7b5b\u57fa\u56e0\u7684\u6b65\u9aa4\u5374\u4e0d\u77e5\u9053\u5982\u4f55\u53bb\u9a8c\u8bc1\u57fa\u56e0\uff0c\u5f80\u5f80\u4f7f\u7528\u7684\u662f\u9884\u540e\u7684\u5206\u6790\uff0c\u5374\u4e0d\u4e86\u89e3\u67d0\u4e2a\u57fa\u56e0\u7684\u529f\u80fd\u4ee5\u53ca\u8c03\u63a7\u7684\u901a\u8def\u3002\u800c\u4e00\u822c\u5728\u524d\u9762\u505a\u5dee\u5f02\u57fa\u56e0\u65f6\u5019\u5c31\u505a\u4e86\u901a\u8def\u5206\u6790\u548cGO\u529f\u80fd\u5bcc\u96c6\uff0c\u4e5f\u53ea\u662f\u6574\u4f53\u57fa\u56e0\u7684\u8c03\u63a7\u548c\u529f\u80fd\u3002\u8fd9\u4e2a\u65f6\u5019\u5c0f\u4f19\u4f34\u60f3\u505a\u5355\u4e2a\u57fa\u56e0\u7684\u5206\u6790\uff0c\u5c31\u63d0\u95ee\u5355\u57fa\u56e0\u7684\u901a\u8def\u5bcc\u96c6\u600e\u4e48\u505a\uff0c\u5c0f\u679c\u5728\u8fd9\u91cc\u6559\u5c0f\u4f19\u4f34\u5982\u4f55\u53bb\u505a\u5355\u57fa\u56e0\u7684GSEA\uff0c\u8ba9\u5c0f\u4f19\u4f34\u4e86\u89e3\u81ea\u5df1\u7b5b\u9009\u57fa\u56e0\u5728\u90a3\u4e9b\u8c03\u63a7\u901a\u8def\u4ee5\u53ca\u5176\u5206\u5b50\u529f\u80fd\u3002<\/p>\n

\u4e0d\u8fc7\u5728\u8fd9\u91cc\u5c0f\u679c\u63d0\u9192\u5c0f\u4f19\u4f34\u4e00\u4e0b\uff0c\u6211\u4eec\u5e76\u4e0d\u662f\u5bf9\u5355\u4e2a\u57fa\u56e0\u505aGSEA\uff0c\u662f\u62ff\u6211\u4eec\u5355\u4e2a\u57fa\u56e0\u76f8\u5173\u7684\u57fa\u56e0\u5bcc\u96c6\u4f5c\u4e3a\u5206\u7ec4\u7684\u65b9\u5f0f\uff0c\u53bb\u63a2\u7a76\u6211\u4eec\u60f3\u8981\u5206\u6790\u57fa\u56e0\u7684\u76f8\u5173\u8c03\u63a7\u7684\u901a\u8def\u3002<\/p>\n

\u8ddf\u7740\u5c0f\u679c\u53bb\u5b66\u4e60\u5427<\/p>\n

library(ggplot2)<\/p>\n

#BiocManager::install(“limma”)<\/p>\n

library(limma)<\/p>\n

library(pheatmap)<\/p>\n

library(ggsci)<\/p>\n

lapply(c(‘clusterProfiler’,’enrichplot’,’patchwork’), function(x) {library(x, character.only = T)})<\/p>\n

library(org.Hs.eg.db)<\/p>\n

library(patchwork)<\/p>\n

\u5728\u8fd9\u91cc\u9700\u8981\u4ee5\u4e0a\u51e0\u4e2a\u5305\uff0c\u5c0f\u4f19\u4f34\u6ca1\u4e0b\u8f7d\u7684\u9700\u8981\u4e0b\u8f7d\u4e00\u4e0b\uff0c\u5176\u4e2dorg.Hs.eg.db\u5c31\u662f\u505a\u5bcc\u96c6\u7684\u5305\u3002\u4f46\u662f\u53ef\u80fd\u4e0d\u592a\u597d\u4e0b\u8f7d\uff0c\u7528\u8fd9\u4e2a\u4ee3\u7801BiocManager::install\u8bd5\u8bd5\u3002<\/p>\n

\u5c0f\u679c\u8fd9\u91cc\u7528\u81ea\u5df1\u793a\u4f8b\u6570\u636e\uff0c\u5c0f\u4f19\u4f34\u6839\u636e\u60c5\u51b5\u81ea\u884c\u8bbe\u7f6e\uff1a<\/p>\n

\u63a5\u4e0b\u6765<\/p>\n

#\u8f93\u5165\u6587\u4ef6<\/p>\n

expFile=”after_merge.txt”<\/p>\n

sgene=”GINS2″ #\u8fd9\u91cc\u8f93\u5165\u8fdb\u884c\u5355\u57fa\u56e0GSEA\u7684\u57fa\u56e0\u540d\u79f0<\/p>\n

\u4e0b\u9762\u6211\u4eec\u5bf9\u8868\u8fbe\u77e9\u9635\u5904\u7406\u4e00\u4e0b\uff0c\u5148\u8bfb\u5165<\/p>\n

rt=read.table(expFile,sep=”\\t”,header=T,check.names=F)<\/p>\n

rt=as.matrix(rt)<\/p>\n

rownames(rt)=rt[,1]#\u628a\u77e9\u9635\u7b2c\u4e00\u5217\u653e\u5728R\u4e2d\u7684\u7b2c\u4e00\u5217<\/p>\n

exp=rt[,2:ncol(rt)]<\/p>\n

dimnames=list(rownames(exp),colnames(exp))<\/p>\n

data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)<\/p>\n

data=avereps(data)<\/p>\n

<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u6309\u7167\u57fa\u56e0\u7684\u8868\u8fbe\u5206\u7ec4<\/p>\n

#\u6309\u57fa\u56e0\u8868\u8fbe\u5206\u7ec4<\/p>\n

group <- ifelse(data[c(sgene),]>median(data[c(sgene),]), “High”, “Low”)<\/p>\n

group <- factor(group,levels = c(“High”,”Low”))<\/p>\n

\u6211\u4eec\u8fd9\u91cc\u662f\u662f\u6ca1\u6709\u505a\u5dee\u5f02\u5206\u6790\u7684\u77e9\u9635\uff0c\u6211\u4eec\u9700\u8981\u627e\u5230\u4e0e\u6211\u4eec\u60f3\u8981\u57fa\u56e0\u76f8\u5173\u7684\uff0c\u5e76\u4e14\u6211\u4eec\u7684\u57fa\u56e0\u4e5f\u9700\u8981\u6709\u5dee\u5f02\u624d\u53ef\u4ee5\u771f\u5b9e\u6027<\/p>\n

\u63a5\u4e0b\u6765\uff1a<\/p>\n

#\u5dee\u5f02\u5206\u6790<\/p>\n

design <- model.matrix(~0+group)<\/p>\n

colnames(design) <- levels(group)<\/p>\n

fit <- lmFit(data,design)<\/p>\n

cont.matrix<-makeContrasts(High-Low,levels=design)<\/p>\n

fit2 <- contrasts.fit(fit, cont.matrix)<\/p>\n

fit2 <- eBayes(fit2)<\/p>\n

deg=topTable(fit2,adjust=’fdr’,number=nrow(data))<\/p>\n

Diff=deg<\/p>\n

#\u4fdd\u5b58\u5355\u57fa\u56e0\u5206\u7ec4\u7684\u6240\u6709\u57fa\u56e0\u5dee\u5f02\u7ed3\u679c<\/p>\n

DIFFOUT=rbind(id=colnames(Diff),Diff)<\/p>\n

write.table(DIFFOUT,file=paste0(“1.”,”DIFF_all.xls”),sep=”\\t”,quote=F,col.names=F)<\/p>\n

#\u5c55\u793a\u5dee\u5f02\u6700\u5927\u7684\u524d30\u4e2a\u57fa\u56e0<\/p>\n

Diff=Diff[order(as.numeric(as.vector(Diff$logFC))),]<\/p>\n

diffGene=as.vector(rownames(Diff))<\/p>\n

diffLength=length(diffGene)<\/p>\n

afGene=c()<\/p>\n

if(diffLength>(60)){<\/p>\n

afGene=diffGene[c(1:30,(diffLength-30+1):diffLength)]<\/p>\n

}else{<\/p>\n

afGene=diffGene<\/p>\n

}<\/p>\n

afExp=data[afGene,]<\/p>\n

\u6211\u4eec\u5bf9\u5206\u7ec4\u8fdb\u884c\u6807\u8bb0\uff0c<\/p>\n

#\u5206\u7ec4\u6807\u7b7e<\/p>\n

Type1=as.data.frame(group)<\/p>\n

Type1=Type1[order(Type1$group,decreasing = T),,drop=F]<\/p>\n

Type=Type1[,1]<\/p>\n

names(Type)=rownames(Type1)<\/p>\n

Type=as.data.frame(Type)<\/p>\n

#\u5206\u7ec4\u6807\u7b7e\u7684\u6ce8\u91ca\u989c\u8272<\/p>\n

anncolor=list(Type=c(High=”red”,Low=”blue” ))<\/p>\n

pdf(file=paste0(“1.”, “DIFF_heatmap.pdf”),height=7,width=6)<\/p>\n

pheatmap(afExp[,rownames(Type1)], #\u70ed\u56fe\u6570\u636e<\/p>\n

annotation=Type, #\u5206\u7ec4<\/p>\n

color = colorRampPalette(c(“blue”,”white”,”red”))(50), #\u70ed\u56fe\u989c\u8272<\/p>\n

cluster_cols =F, #\u4e0d\u6dfb\u52a0\u5217\u805a\u7c7b\u6811<\/p>\n

show_colnames = F, #\u5c55\u793a\u5217\u540d<\/p>\n

scale=”row”,<\/p>\n

fontsize = 10,<\/p>\n

fontsize_row=6,<\/p>\n

fontsize_col=8,<\/p>\n

annotation_colors=anncolor<\/p>\n

)<\/p>\n

dev.off()<\/p>\n

#\u706b\u5c71\u56fe\u5dee\u5f02\u6807\u51c6\u8bbe\u7f6e\uff0c\u8fd9\u4e2a\u5c0f\u4f19\u4f34\u81ea\u884c\u8bbe\u7f6e\uff0c\u5e76\u4e0d\u662f\u4e00\u5b9a\u8981\u7ed8\u5236<\/p>\n

adjP=0.05<\/p>\n

aflogFC=0.5<\/p>\n

Significant=ifelse((Diff$P.Value<adjP & abs(Diff$logFC)>aflogFC), ife<\/p>\n

#\u5f00\u59cb\u7ed8\u5236<\/p>\n

p = ggplot(Diff, aes(logFC, -log10(P.Value)))+<\/p>\n

geom_point(aes(col=Significant),size=4)+ #size\u70b9\u7684\u5927\u5c0f<\/p>\n

scale_color_manual(values=c(pal_npg()(2)[2], “#838B8B”, pal_npg()(1)))+<\/p>\n

labs(title = ” “)+<\/p>\n

theme(plot.title = element_text(size = 16, hjust = 0.5, face = “bold”))+<\/p>\n

geom_hline(aes(yintercept=-log10(adjP)), colour=”gray”, linetype=”twodash”,size=1)+<\/p>\n

geom_vline(aes(xintercept=aflogFC), colour=”gray”, linetype=”twodash”,size=1)+<\/p>\n

geom_vline(aes(xintercept=-aflogFC), colour=”gray”, linetype=”twodash”,size=1)<\/p>\n

#\u67e5\u770b\uff0c\u4e0d\u6dfb\u52a0\u6807\u8bb0\u53ef\u4ee5\u76f4\u63a5\u4fdd\u5b58<\/p>\n

p<\/p>\n

#\u6dfb\u52a0\u57fa\u56e0\u70b9\u6807\u8bb0\uff0c\u6309\u7167,\u53ef\u81ea\u884c\u6839\u636e\u5dee\u5f02\u5206\u6790\u7684\u7ed3\u679c\u8fdb\u884c\u6807\u8bb0<\/p>\n

point.Pvalue=0.01<\/p>\n

point.logFc=1.5<\/p>\n

\u5176\u5b9e\u4ee5\u4e0a\u5c0f\u679c\u4e5f\u6559\u4e86\u5c0f\u4f19\u4f34\u706b\u5c71\u56fe\u7684\u7ed8\u5236<\/p>\n

\u6211\u4eec\u76f4\u63a5\u8bfb\u53d6\u7ed3\u679c\uff0c\u7528\u4e0b\u9762\u7ed3\u679c\u53bb\u5206\u6790<\/p>\n

logFC_t=0<\/p>\n

deg$g=ifelse(deg$P.Value>0.05,’stable’,<\/p>\n

ifelse( deg$logFC > logFC_t,’UP’,<\/p>\n

ifelse( deg$logFC < -logFC_t,’DOWN’,’stable’) )<\/p>\n

)<\/p>\n

table(deg$g)<\/p>\n

deg$symbol=rownames(deg)<\/p>\n

df <- bitr(unique(deg$symbol), fromType = “SYMBOL”,<\/p>\n

toType = c( “ENTREZID”),<\/p>\n

OrgDb = org.Hs.eg.db)<\/p>\n

DEG=deg<\/p>\n

DEG=merge(DEG,df,by.y=’SYMBOL’,by.x=’symbol’)<\/p>\n

data_all_sort <- DEG %>%<\/p>\n

arrange(desc(logFC))<\/p>\n

<\/p>\n

\u4e0a\u8ff0\u5c31\u662f\u5c06\u5dee\u5f02\u7684\u57fa\u56e0\u7ed8\u5236\u6210\u706b\u5c71\u56fe\uff0c\u800c\u6211\u4eec\u5355\u72ec\u505a\u7684\u57fa\u56e0\u4e5f\u5305\u62ec\u5728\u5185<\/p>\n

geneList = data_all_sort$logFC #\u628afoldchange\u6309\u7167\u4ece\u5927\u5230\u5c0f\u63d0\u53d6\u51fa\u6765<\/p>\n

names(geneList) <- data_all_sort$ENTREZID #\u7ed9\u4e0a\u9762\u63d0\u53d6\u7684foldchange\u52a0\u4e0a\u5bf9\u5e94\u4e0aENTREZID<\/p>\n

head(geneList)<\/p>\n

\u4e0a\u8ff0\u5c31\u662f\u5dee\u5f02\u5206\u6790\u7684\u8fc7\u7a0b\uff0c\u63a5\u4e0b\u6765\u5c31\u662f\u6211\u4eec\u7684\u91cd\u5934\u620f\uff1a<\/p>\n

#\u5f00\u59cb\u5bcc\u96c6\u5206\u6790<\/p>\n

kk2 <- gseKEGG(geneList = geneList,<\/p>\n

organism = ‘hsa’,<\/p>\n

nPerm = 10000,<\/p>\n

minGSSize = 10,<\/p>\n

maxGSSize = 200,<\/p>\n

pvalueCutoff = 0.05,<\/p>\n

pAdjustMethod = “none” )<\/p>\n

class(kk2)<\/p>\n

colnames(kk2@result)<\/p>\n

kegg_result <- as.data.frame(kk2)<\/p>\n

rownames(kk2@result)[head(order(kk2@result$enrichmentScore))]<\/p>\n

af=as.data.frame(kk2@result)<\/p>\n

write.table(af,file=paste0(“2.”,”all_GSEA.xls”),sep=”\\t”,quote=F,col.names=T)#\u8fd9\u91cc\u6211\u4eec\u628a\u7ed3\u679c\u4fdd\u5b58\u4e00\u4e0b<\/p>\n

<\/p>\n

\u8fd9\u4e2a\u5c31\u662f\u7ed3\u679c\uff0c\u4e0b\u9762\u6211\u5c06\u4ed6\u53ef\u89c6\u5316\uff01<\/p>\n

#\u6392\u5e8f\u540e\u5206\u522b\u53d6GSEA\u7ed3\u679c\u7684\u524d5\u4e2a\u548c\u540e5\u4e2a<\/p>\n

num=5<\/p>\n

pdf(paste0(“2.”,”down_GSEA.pdf”),width = 8,height = 8)<\/p>\n

gseaplot2(kk2, geneSetID = rownames(kk2@result)[head(order(kk2@result$enrichmentScore),num)])<\/p>\n

dev.off()<\/p>\n

pdf(paste0(“2.”,”up_GSEA.pdf”),width = 8,height = 8)<\/p>\n

gseaplot2(kk2, geneSetID = rownames(kk2@result)[tail(order(kk2@result$enrichmentScore),num)])<\/p>\n

dev.off()<\/p>\n

\u8fd9\u91cc\u5c06\u4e0a\u8c03\u7684\u548c\u4e0b\u8c03\u7684\u5206\u6790\u51fa\u56fe<\/p>\n

<\/p>\n

\u4e5f\u53ef\u5c06\u6240\u4ee5\u7684\u90fd\u653e\u5728\u4e00\u8d77<\/p>\n

#\u6392\u5e8f\u540e\u53d6\u524d5\u4e2a\u548c\u540e5\u4e2a\u4e00\u8d77\u5c55\u793a<\/p>\n

num=5<\/p>\n

pdf(paste0(“2.”,”all_GSEA.pdf”),width = 10,height = 10)<\/p>\n

gseaplot2(kk2, geneSetID = rownames(kk2@result)[c(head(order(kk2@result$enrichmentScore),num),tail(order(kk2@result$enrichmentScore),num))])<\/p>\n

dev.off()<\/p>\n

<\/p>\n

\u6211\u4eec\u8fd8\u53ef\u4ee5\u5176\u5b83\u7684\u5f62\u5f0f\u53bb\u5c55\u793a\u7ed3\u679c\uff1a<\/p>\n

\u8fd9\u4f8b\u6211\u4eec\u5c06\u5355\u72ec\u5bcc\u96c6\u7684#\u5355\u72ec\u5c55\u793a,\u5c0f\u4f19\u4f34\u81ea\u884c\u4fdd\u5b58<\/p>\n

gseaplot2(kk2,<\/p>\n

title = “Th1 and Th2 cell differentiation”, #\u8bbe\u7f6e\u6807\u9898<\/p>\n

“hsa04658″, #\u7ed8\u5236hsa04658\u901a\u8def\u7684\u7ed3\u679c\uff0c\u901a\u8def\u540d\u79f0\u4e0e\u7f16\u53f7\u5bf9\u5e94<\/p>\n

color=”red”, #\u7ebf\u6761\u989c\u8272<\/p>\n

base_size = 20, #\u57fa\u7840\u5b57\u4f53\u7684\u5927\u5c0f<\/p>\n

subzplots = 1:3,<\/p>\n

pvalue_table = T) # \u663e\u793ap\u503c<\/p>\n

\u5c0f\u4f19\u4f34\u6839\u636e\u81ea\u5df1\u60f3\u8981\u7684\u901a\u8def\u53bb\u7ed8\u5236\u5427<\/p>\n

\u4ee5\u4e0a\u5c31\u662fGSEA\u7684\u7ed8\u5236\u4e86\uff0c\u5c0f\u4f19\u4f34\u6709\u6ca1\u6709\u5b66\u4f1a\u5462\uff0c\u7ed8\u5236\u7684\u4ee3\u7801\u5176\u5b9e\u4e0d\u590d\u6742\uff0c\u4e2d\u95f4\u53c2\u6742\u4e86\u5dee\u5f02\u5206\u6790\uff0c\u770b\u8d77\u5f88\u591a\uff0c\u5176\u5b9e\u7cbe\u9ad3\u90fd\u5728\u540e\u9762\u7684GSEA\u5206\u6790\u4ee3\u7801\u4e2d\uff0c\u5c0f\u4f19\u4f34\u8981\u591a\u591a\u7406\u89e3\u4ee3\u7801\u7684\u610f\u601d\uff0c\u8fd9\u6837\u624d\u80fd\u7ed8\u5236\u51fa\u81ea\u5df1\u60f3\u8981\u7684\u56fe\u7247<\/p>\n","protected":false},"excerpt":{"rendered":"

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