{"id":26308,"date":"2024-01-25T12:20:23","date_gmt":"2024-01-25T04:20:23","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26308"},"modified":"2024-01-25T12:20:25","modified_gmt":"2024-01-25T04:20:25","slug":"%e7%ba%af%e4%bb%a3%e7%a0%81%e5%ae%9e%e6%93%8d%ef%bc%8c%e5%b0%8f%e6%9e%9c%e5%b8%a6%e4%bd%a0%e8%ae%a1%e7%ae%97%e5%9f%ba%e5%9b%a0%e4%b8%8e%e5%85%8d%e7%96%ab%e7%bb%86%e8%83%9e%e5%9c%a8tmb%e4%b8%ad","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26308","title":{"rendered":"\u7eaf\u4ee3\u7801\u5b9e\u64cd\uff0c\u5c0f\u679c\u5e26\u4f60\u8ba1\u7b97\u57fa\u56e0\u4e0e\u514d\u75ab\u7ec6\u80de\u5728TMB\u4e2d\u7684\u76f8\u5173\u6027"},"content":{"rendered":"

\u6211\u4eec\u5927\u5bb6\u77e5\u9053\u57fa\u56e0\u4e0e\u591a\u79cd\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u4e4b\u95f4\u6709\u7740\u76f8\u5173\u6027\uff0c\u90a3\u4e48\u4e86\u89e3\u6307\u5b9a\u57fa\u56e0\u4e0e\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u4ee5\u53ca\u662f\u5426\u5b58\u5728\u76f8\u5173\u6027\uff0c\u4ee5\u53ca\u57fa\u56e0\u5728\u8c03\u8282\u514d\u75ab\u53cd\u5e94\u4e2d\u7684\u6f5c\u5728\u529f\u80fd\u662f\u5f88\u91cd\u8981\u7684\u3002\u90a3\u4e48\u4e0b\u9762\u6211\u4eec\u5148\u4e86\u89e3\u4e00\u4e0br\u5305\u7684\u6982\u5ff5\u3002<\/p>\n

CIBERSORT<\/strong>\u662f\u4e00\u79cd\u5e38\u7528\u4e8e\u89e3\u6790\u80bf\u7624\u7ec4\u7ec7\u4e2d\u4e0d\u540c\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u5206\u5e03\u7684\u7b97\u6cd5\u3002\u901a\u8fc7\u5c06CIBERSORT\u7ed3\u679c\u4e0e\u7279\u5b9a\u57fa\u56e0\u7684\u8868\u8fbe\u6570\u636e\u8fdb\u884c\u76f8\u5173\u6027\u5206\u6790\uff0c\u53ef\u4ee5\u63ed\u793a\u8fd9\u4e9b\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u5728\u57fa\u56e0\u8c03\u63a7\u8fc7\u7a0b\u4e2d\u7684\u6f5c\u5728\u91cd\u8981\u6027\u3002<\/p>\n

\u672c\u671f\u4e3b\u8981\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u5355\u57fa\u56e0\u4e0e\u591a\u79cd\u514d\u75ab\u7ec6\u80de\u7684\u76f8\u5173\u6027\u5206\u6790\u7684\u5b9e\u64cd\u4fdd\u59c6\u7ea7\u4ee3\u7801\u3002<\/strong><\/p>\n

\u4ee3\u7801\u4e2d\u5bf9\u793a\u4f8b\u57fa\u56e0\u7684<\/strong>CXCR4\u7684\u8868\u8fbe\u91cf\u8fdb\u884c\u514d\u75ab\u76f8\u5173\u5206\u6790\u3002\u4e0b\u9762\u662f\u4e0b\u8f7d\u6574\u7406\u7684\u6837\u672c\u8868\u8fbe\u91cf\u4fe1\u606f\u548c\u6837\u672c\u514d\u75ab\u7ec6\u80de\u4fe1\u606f\u3002<\/strong><\/p>\n

<\/p>\n

<\/p>\n

#if (!requireNamespace(“BiocManager”, quietly = TRUE))<\/p>\n

# install.packages(“BiocManager”)<\/p>\n

#BiocManager::install(“limma”)<\/p>\n

#\u68c0\u67e5\u662f\u5426\u5b89\u88c5\u4e86\u201cBiocManager\u201d\u5305\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u5b83\u4f1a\u8fdb\u884c\u5b89\u88c5\u3002BiocManager\u7528\u4e8e\u5728R\u4e2d\u7ba1\u7406Bioconductor\u5305\u3002<\/p>\n

#install.packages(“ggplot2”)<\/p>\n

#install.packages(“ggpubr”)<\/p>\n

#install.packages(“ggExtra”)<\/p>\n

library(limma)<\/p>\n

library(ggplot2)<\/p>\n

library(ggpubr)<\/p>\n

library(ggExtra)<\/p>\n

#\u5b89\u88c5\u548c\u52a0\u8f7d\u6240\u9700\u7684R\u5305\uff0c\u4f8b\u5982\u201climma\u201d\u3001\u201cggplot2\u201d\u3001\u201cggpubr\u201d\u548c\u201cggExtra\u201d\u3002\u8fd9\u4e9b\u5305\u7528\u4e8e\u5404\u79cd\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u4efb\u52a1\u3002<\/p>\n

immuneFile=”CIBERSORT-Results.txt” <\/strong> #\u514d\u75ab\u7ed3\u679c\u6587\u4ef6<\/p>\n

expFile=”symbol.txt” <\/strong> #\u8868\u8fbe\u8f93\u5165\u6587\u4ef6<\/p>\n

gene=”CXCR4″ #\u57fa\u56e0\u540d\u79f0<\/p>\n

pFilter=0.05 #CIBERSORT\u7ed3\u679c\u8fc7\u6ee4\u6761\u4ef6<\/p>\n

#\u5982\u201cimmuneFile\u201d\uff08\u5305\u542bCIBERSORT\u7ed3\u679c\u7684\u6587\u4ef6\uff09\u3001<\/p>\n

#\u201cexpFile\u201d\uff08\u8868\u8fbe\u5f0f\u8f93\u5165\u6587\u4ef6\uff09\u3001\u201cgene\u201d\uff08\u611f\u5174\u8da3\u7684\u7279\u5b9a\u57fa\u56e0\uff09\u548c\u201cpFilter\u201d\uff08CIBERSORT\u7ed3\u679c\u8fc7\u6ee4\u9608\u503c\uff09\u3002<\/p>\n

#\u8bfb\u53d6\u514d\u75ab\u7ed3\u679c\u6587\u4ef6\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u6574\u7406<\/p>\n

immune=read.table(immuneFile,sep=”\\t”,header=T,row.names=1,check.names=F)<\/strong><\/p>\n

immune=immune[immune[,”P-value”]<pFilter,]<\/p>\n

immune=as.matrix(immune[,1:(ncol(immune)-3)])<\/p>\n

#\u4ece\u201cimmuneFile\u201d\u4e2d\u8bfb\u53d6CIBERSORT\u7ed3\u679c\uff0c<\/p>\n

#\u5e76\u8fc7\u6ee4\u6389p\u503c\u5c0f\u4e8e\u6307\u5b9a\u9608\u503c\uff08\u201cpFilter\u201d\uff09\u7684\u884c\u3002\u8fd8\u4ece\u201cimmune\u201d\u6570\u636e\u6846\u4e2d\u5220\u9664\u4e0d\u5fc5\u8981\u7684\u5217\u3002<\/p>\n

#\u8bfb\u53d6\u8868\u8fbe\u6587\u4ef6\uff0c\u5e76\u5bf9\u8f93\u5165\u6587\u4ef6\u6574\u7406<\/strong><\/p>\n

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

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

rownames(rt)=rt[,1]<\/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

#\u4ece\u201cexpFile\u201d\u4e2d\u8bfb\u53d6\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u63d0\u53d6\u6307\u5b9a\u201cgene\u201d\u7684\u76f8\u5173\u6570\u636e\uff0c<\/p>\n

#\u5e76\u5904\u7406\u6570\u636e\u4ee5\u53bb\u9664\u6b63\u5e38\u6837\u672c\u5e76\u9009\u62e9\u4e0e\u514d\u75ab\u6570\u636e\u4ea4\u96c6\u7684\u6837\u672c\u3002<\/p>\n

#\u7136\u540e\uff0c\u9009\u62e9\u9664\u7b2c\u4e00\u5217\u5916\u7684\u6240\u6709\u5217\u4f5c\u4e3a\u8868\u8fbe\u6570\u636e\uff0c\u5e76\u91cd\u65b0\u5b9a\u4e49\u7ef4\u5ea6\u540d\u79f0\u3002\u6700\u540e\uff0c\u6570\u636e\u8fdb\u884c\u4e86\u5e73\u5747\u5904\u7406\u3002<\/p>\n

group=sapply(strsplit(colnames(data),”\\\\-“),”[“,4)<\/p>\n

group=sapply(strsplit(group,””),”[“,1)<\/p>\n

group=gsub(“2″,”1”,group)<\/p>\n

data=data[,group==0]<\/p>\n

#\u53bb\u9664\u6b63\u5e38\u6837\u54c1\uff0c\u6839\u636e\u6837\u672c\u540d\u4e2d\u7684\u4fe1\u606f\u5224\u65ad\u662f\u5426\u4e3a\u6b63\u5e38\u6837\u672c\uff0c\u5e76\u53bb\u9664\u4e86\u6b63\u5e38\u6837\u672c\uff0c\u4fdd\u7559\u4ec5\u5305\u542b\u80bf\u7624\u6837\u672c\u7684\u6570\u636e\u3002<\/p>\n

#\u6837\u54c1\u53d6\u4ea4\u96c6\u5c06\u57fa\u56e0\u7684\u8868\u8fbe\u503c\u6dfb\u52a0\u5230\u514d\u75ab\u6570\u636e\u4e2d\uff0c\u7136\u540e\u53d6\u514d\u75ab\u6570\u636e\u548c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u4ea4\u96c6\uff0c<\/p>\n

#\u4ee5\u786e\u4fdd\u4e24\u8005\u5177\u6709\u76f8\u540c\u7684\u6837\u672c\u3002\u8fd9\u6837\u53ef\u4ee5\u4fdd\u8bc1\u514d\u75ab\u6570\u636e\u548c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e4b\u95f4\u6709\u76f8\u540c\u7684\u6837\u672c\uff0c\u4fbf\u4e8e\u540e\u7eed\u76f8\u5173\u6027\u5206\u6790\u3002<\/p>\n

data=rbind(data,gene=data[gene,])<\/p>\n

exp=t(data[c(“gene”,gene),])<\/p>\n

sameSample=intersect(row.names(immune),row.names(exp))<\/p>\n

immune1=immune[sameSample,]<\/p>\n

exp1=exp[sameSample,]<\/p>\n

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

x=as.numeric(exp1[,1])<\/p>\n

#\u6309\u514d\u75ab\u7ec6\u80de\u5faa\u73af<\/p>\n

for(j in colnames(immune1)[1:22]){<\/p>\n

y=as.numeric(immune1[,j])<\/p>\n

if(sd(y)>0.001){<\/p>\n

df1=as.data.frame(cbind(x,y))<\/p>\n

corT=cor.test(x,y,method=”spearman”)<\/p>\n

cor=corT$estimate<\/p>\n

pValue=corT$p.value<\/p>\n

p1=ggplot(df1, aes(x, y)) +<\/p>\n

ylab(j)+xlab(gene)+<\/p>\n

geom_point()+ geom_smooth(method=”lm”,formula=y~x) + theme_bw()+<\/p>\n

stat_cor(method = ‘spearman’, aes(x =x, y =y))<\/p>\n

if(pValue<pFilter){<\/p>\n

pdf(file=paste0(j,”.pdf”),width=5,height=5)<\/p>\n

print(p1)<\/p>\n

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

outTab=rbind(outTab,cbind(Cell=j,pValue))<\/p>\n

}<\/p>\n

}<\/p>\n

}<\/p>\n

#\u76f8\u5173\u6027\u68c0\u9a8c\u8fdb\u884c\u76f8\u5173\u6027\u68c0\u9a8c\uff0c\u8ba1\u7b97\u57fa\u56e0\u8868\u8fbe\u503c\uff08x\uff09\u4e0e\u5404\u79cd\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u6bd4\u4f8b\uff08y\uff09\u4e4b\u95f4\u7684Spearman\u79e9\u76f8\u5173\u7cfb\u6570\u3002<\/p>\n

#\u5faa\u73af\u904d\u5386\u6bcf\u79cd\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\uff0c\u5e76\u7ed8\u5236\u6563\u70b9\u56fe\u548c\u56de\u5f52\u7ebf\uff0c\u4ee5\u53ca\u6807\u6ce8\u76f8\u5173\u7cfb\u6570\u548cp\u503c\u3002<\/p>\n

#\u5982\u679c\u76f8\u5173\u6027\u663e\u8457\uff08pValue<pFilter\uff09\uff0c\u5219\u5c06\u6563\u70b9\u56fe\u4fdd\u5b58\u4e3a\u4ee5\u514d\u75ab\u7ec6\u80de\u7c7b\u578b\u547d\u540d\u7684PDF\u6587\u4ef6\uff0c<\/p>\n

#\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728\u6570\u636e\u6846”outTab”\u4e2d\u3002\u6700\u540e\uff0c\u5c06\u5305\u542b\u663e\u8457\u76f8\u5173\u6027\u7684\u7ed3\u679c\u5b58\u50a8\u5728\u540d\u4e3a”immuneCor.result.txt”\u7684\u6587\u672c\u6587\u4ef6\u4e2d<\/p>\n

write.table(outTab,file=”immuneCor.result.txt”,sep=”\\t”,row.names=F,quote=F)<\/strong><\/p>\n

<\/p>\n

<\/p>\n

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\u4e91\u751f\u4fe1\u5e73\u53f0\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002<\/a><\/p>\n

\u4e91\u751f\u4fe1\u5e73\u53f0\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

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