如何分析GDC数据库中的数据的R语言包GDC RNATools

GDCRNATools：加利福尼亚大学生物与植物科学系植物基因组学中LNCRNA、miRNA和mRNA数据的综合分析软件包

GDC：基因组数据共享

• 数据下载
• ceRNA网络分析
• 差异表达分析
• 功能富集分析
• 生存分析
• 数据可视化 火山图、热图、GO富集分析结果、KEGG富集分析结果等

帮助文档链接 http://bioconductor.org/packages/devel/bioc/vignettes/GDCRNATools/inst/doc/GDCRNATools.html

library(GDCRNATools)
project<-'TCGA-CHOL'
rnadir<-paste(project,'RNAseq',sep='/')
mirdir<-paste(project,'miRNAs',sep="/")
gdcRNADownload(project.id = 'TCGA-CHOL',
               data.type = 'RNAseq',
               write.manifest = F,
               method = 'gdc-client',
               directory = rnadir)

在linux系统中重复到这一步的时候遇到报错 ImportError: /lib64/libc.so.6: version `GLIBC_2.18' not found (required by /tmp/_MEIylVP0W/libstdc++

我的解决办法是把它默认下载的gdc-client_v1.3.0替换掉，我换成gdc-client_v1.5.0，下载地址是https://gdc.cancer.gov/access-data/gdc-data-transfer-tool

gdcRNADownload(project.id = 'TCGA-CHOL',
               data.type = 'miRNAs',
               write.manifest = F,
               method = 'gdc-client',
               directory = mirdir)
clinicaldir<-paste(project,'Clinical',sep='/')
gdcClinicalDownload(project.id = 'TCGA-CHOL',
                    write.manifest = F,
                    method='gdc-client',
                    directory = clinicaldir)
metaMatrix.RNA<-gdcParseMetadata(project.id = 'TCGA-CHOL',
                                 data.type = 'RNAseq',
                                 write.meta = F)
metaMatrix.RNA<-gdcFilterDuplicate(metaMatrix.RNA)
metaMatrix.RNA<-gdcFilterSampleType(metaMatrix.RNA)

metaMatrix.MIR<-gdcParseMetadata(project.id = 'TCGA-CHOL',
                                 data.type = 'miRNAs',
                                 write.meta = F)
metaMatrix.MIR

metaMatrix.MIR<-gdcFilterDuplicate(metaMatrix.MIR)
metaMatrix.MIR<-gdcFilterSampleType(metaMatrix.MIR)

获取表达矩阵

rnaCounts<-gdcRNAMerge(metadata = metaMatrix.RNA,
                       path = rnadir,
                       organized = FALSE,
                       data.type = 'RNAseq')
mirCounts<-gdcRNAMerge(metadata = metaMatrix.MIR,
                       path = mirdir,
                       organized = FALSE,
rnaCounts[1:5,1:5]
mirCounts[1:5,1:5]

标准化表达数据

rnaExpr<-gdcVoomNormalization(counts=rnaCounts,filter=F)
mirExpr<-gdcVoomNormalization(counts=mirCounts,filter=F)
rnaExpr[1:5,1:5]
mirExpr[1:5,1:5]

差异表达分析

DEGAll<-gdcDEAnalysis(counts = rnaCounts,
                      group=metaMatrix.RNA$sample_type,
                      comparison = 'PrimaryTumor-SolidTissueNormal',
                      method='limma')
deALL<-gdcDEReport(deg=DEGAll,gene.type = 'all')
deLNC<-gdcDEReport(deg=DEGAll,gene.type='long_non_coding')
dePC<-gdcDEReport(deg=DEGAll,gene.type = 'protein_coding')

记下来是数据可视化展示

gdcBarPlot(deg=deALL,angle = 45,data.type = 'RNAseq')

这里TEC和IG分别是啥？

gdcVolcanoPlot(deLNC)

degName<-rownames(deLNC)
gdcHeatmap(deg.id = degName,metadata = metaMatrix.RNA,rna.expr = rnaExpr)

enrichOutput<-gdcEnrichAnalysis(gene=rownames(deALL),
                                simplify=T)
gdcEnrichPlot(enrichOutput,type='bar',category = 'GO',num.terms = 10)

画图的时候遇到报错 Error in .Call.graphics(C_palette2, .Call(C_palette2, NULL)) : invalid graphics state 不知道原因出在哪里，但是保存到本地没问题

pdf(file="../goenrich.pdf",width = 15,height = 15)
gdcEnrichPlot(enrichOutput,type='bar',category = 'GO',num.terms = 10)
dev.off()

ceOUtput<-gdcCEAnalysis(lnc=rownames(deLNC),
                        pc=rownames(dePC),
                        lnc.targets = 'starBase',
                        pc.targets = 'starBase',
                        rna.expr = rnaExpr,
                        mir.expr = mirExpr)
edges<-gdcExportNetwork(ceNetwork = ceOutput2,net='edges')
nodes<-gdcExportNetwork(ceNetwork = ceOutput2,net='nodes')
write.table(edges,file='edges.txt',sep='\t',quote=F)
write.table(nodes,file="nodes.txt",sep="\t",quote=F)

最后生成了两个文件，如何用cytoscape可视化这两个文件我暂时还不知道如何实现。