## ----installation3, eval=FALSE, message=FALSE, warning=FALSE------------- # source("https://bioconductor.org/biocLite.R") # biocLite("GDCRNATools") ## ----load, eval=TRUE, message=FALSE, warning=FALSE----------------------- library(GDCRNATools) ## ----load data q, message=FALSE, warning=FALSE, eval=TRUE---------------- library(DT) ### load RNA counts data data(rnaCounts) ### load miRNAs counts data data(mirCounts) ## ----normalization q, message=FALSE, warning=FALSE, eval=TRUE------------ ####### Normalization of RNAseq data ####### rnaExpr <- gdcVoomNormalization(counts = rnaCounts, filter = FALSE) ####### Normalization of miRNAs data ####### mirExpr <- gdcVoomNormalization(counts = mirCounts, filter = FALSE) ## ----parse meta2 q, message=FALSE, warning=FALSE, eval=TRUE-------------- ####### Parse and filter RNAseq metadata ####### metaMatrix.RNA <- gdcParseMetadata(project.id = 'TCGA-CHOL', data.type = 'RNAseq', write.meta = FALSE) metaMatrix.RNA <- gdcFilterDuplicate(metaMatrix.RNA) metaMatrix.RNA <- gdcFilterSampleType(metaMatrix.RNA) datatable(as.data.frame(metaMatrix.RNA[1:5,]), extensions = 'Scroller', options = list(scrollX = TRUE, deferRender = TRUE, scroller = TRUE)) ## ----deg q, message=FALSE, warning=FALSE, eval=TRUE---------------------- DEGAll <- gdcDEAnalysis(counts = rnaCounts, group = metaMatrix.RNA$sample_type, comparison = 'PrimaryTumor-SolidTissueNormal', method = 'limma') datatable(as.data.frame(DEGAll), options = list(scrollX = TRUE, pageLength = 5)) ### All DEGs deALL <- gdcDEReport(deg = DEGAll, gene.type = 'all') ### DE long-noncoding deLNC <- gdcDEReport(deg = DEGAll, gene.type = 'long_non_coding') ### DE protein coding genes dePC <- gdcDEReport(deg = DEGAll, gene.type = 'protein_coding') ## ----ce q, message=TRUE, warning=FALSE, eval=TRUE------------------------ ceOutput <- gdcCEAnalysis(lnc = rownames(deLNC), pc = rownames(dePC), lnc.targets = 'starBase', pc.targets = 'starBase', rna.expr = rnaExpr, mir.expr = mirExpr) datatable(as.data.frame(ceOutput), options = list(scrollX = TRUE, pageLength = 5)) ## ----sig q, message=FALSE, warning=FALSE, eval=TRUE---------------------- ceOutput2 <- ceOutput[ceOutput$hyperPValue<0.01 & ceOutput$corPValue<0.01 & ceOutput$regSim != 0,] ## ----edges q, message=FALSE, warning=FALSE, eval=TRUE-------------------- ### Export edges edges <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'edges') datatable(as.data.frame(edges), options = list(scrollX = TRUE, pageLength = 5)) ## ----nodes q, message=FALSE, warning=FALSE, eval=TRUE-------------------- ### Export nodes nodes <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'nodes') datatable(as.data.frame(nodes), options = list(scrollX = TRUE, pageLength = 5)) ## ----manual, eval=FALSE, message=FALSE, warning=FALSE-------------------- # project <- 'TCGA-CHOL' # rnadir <- paste(project, 'RNAseq', sep='/') # mirdir <- paste(project, 'miRNAs', sep='/') # # ####### Download RNAseq data ####### # gdcRNADownload(project.id = 'TCGA-CHOL', # data.type = 'RNAseq', # write.manifest = FALSE, # directory = rnadir) # # ####### Download miRNAs data ####### # gdcRNADownload(project.id = 'TCGA-CHOL', # data.type = 'miRNAs', # write.manifest = FALSE, # directory = mirdir) # ## ----parse meta2, message=FALSE, warning=FALSE, eval=TRUE---------------- ####### Parse RNAseq metadata ####### metaMatrix.RNA <- gdcParseMetadata(project.id = 'TCGA-CHOL', data.type = 'RNAseq', write.meta = FALSE) ####### Filter duplicated samples in RNAseq metadata ####### metaMatrix.RNA <- gdcFilterDuplicate(metaMatrix.RNA) ####### Filter non-Primary Tumor and non-Solid Tissue Normal samples in RNAseq metadata ####### metaMatrix.RNA <- gdcFilterSampleType(metaMatrix.RNA) ## ----parse meta3, message=FALSE, warning=FALSE, eval=TRUE---------------- ####### Parse miRNAs metadata ####### metaMatrix.MIR <- gdcParseMetadata(project.id = 'TCGA-CHOL', data.type = 'miRNAs', write.meta = FALSE) ####### Filter duplicated samples in miRNAs metadata ####### metaMatrix.MIR <- gdcFilterDuplicate(metaMatrix.MIR) ####### Filter non-Primary Tumor and non-Solid Tissue Normal samples in miRNAs metadata ####### metaMatrix.MIR <- gdcFilterSampleType(metaMatrix.MIR) ## ----merge RNAseq, message=FALSE, warning=FALSE, eval=FALSE-------------- # ####### Merge RNAseq data ####### # rnaCounts <- gdcRNAMerge(metadata = metaMatrix.RNA, # path = rnadir, # data.type = 'RNAseq') # # ####### Merge miRNAs data ####### # mirCounts <- gdcRNAMerge(metadata = metaMatrix.MIR, # path = mirdir, # data.type = 'miRNAs') ## ----normalization, message=FALSE, warning=FALSE, eval=FALSE------------- # ####### Normalization of RNAseq data ####### # rnaExpr <- gdcVoomNormalization(counts = rnaCounts, filter = FALSE) # # ####### Normalization of miRNAs data ####### # mirExpr <- gdcVoomNormalization(counts = mirCounts, filter = FALSE) ## ----deg, message=FALSE, warning=FALSE, eval=FALSE----------------------- # DEGAll <- gdcDEAnalysis(counts = rnaCounts, # group = metaMatrix.RNA$sample_type, # comparison = 'PrimaryTumor-SolidTissueNormal', # method = 'limma') ## ----data, message=FALSE, warning=FALSE, eval=TRUE----------------------- data(DEGAll) ## ----extract, message=FALSE, warning=FALSE, eval=TRUE-------------------- ### All DEGs deALL <- gdcDEReport(deg = DEGAll, gene.type = 'all') ### DE long-noncoding deLNC <- gdcDEReport(deg = DEGAll, gene.type = 'long_non_coding') ### DE protein coding genes dePC <- gdcDEReport(deg = DEGAll, gene.type = 'protein_coding') ## ----ce, message=FALSE, warning=FALSE, eval=FALSE------------------------ # ceOutput <- gdcCEAnalysis(lnc = rownames(deLNC), # pc = rownames(dePC), # lnc.targets = 'starBase', # pc.targets = 'starBase', # rna.expr = rnaExpr, # mir.expr = mirExpr) ## ----ce 2, message=FALSE, warning=FALSE, eval=TRUE----------------------- ### load miRNA-lncRNA interactions data(lncTarget) ### load miRNA-mRNA interactions data(pcTarget) pcTarget[1:3] ## ----ce 22, message=FALSE, warning=FALSE, eval=FALSE--------------------- # ceOutput <- gdcCEAnalysis(lnc = rownames(deLNC), # pc = rownames(dePC), # lnc.targets = lncTarget, # pc.targets = pcTarget, # rna.expr = rnaExpr, # mir.expr = mirExpr) ## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------- # ceOutput2 <- ceOutput[ceOutput$hyperPValue<0.01 & # ceOutput$corPValue<0.01 & ceOutput$regSim != 0,] # # 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) ## ----shiny cor plot, eval=FALSE, echo=TRUE, message=FALSE, warning=FALSE---- # shinyCorPlot(gene1 = rownames(deLNC), # gene2 = rownames(dePC), # rna.expr = rnaExpr, # metadata = metaMatrix.RNA) ## ----survival, message=FALSE, warning=FALSE, eval=FALSE------------------ # ####### CoxPH analysis ####### # survOutput <- gdcSurvivalAnalysis(gene = rownames(deALL), # method = 'coxph', # rna.expr = rnaExpr, # metadata = metaMatrix.RNA) ## ----survival2, message=FALSE, warning=FALSE, eval=FALSE----------------- # ####### KM analysis ####### # survOutput <- gdcSurvivalAnalysis(gene = rownames(deALL), # method = 'KM', # rna.expr = rnaExpr, # metadata = metaMatrix.RNA, # sep = 'median') ## ----shiny km plot, eval=FALSE, echo=TRUE, message=FALSE, warning=FALSE, eval=FALSE---- # shinyKMPlot(gene = rownames(deALL), rna.expr = rnaExpr, # metadata = metaMatrix.RNA) ## ----enrichment, message=FALSE, warning=FALSE, eval=FALSE---------------- # enrichOutput <- gdcEnrichAnalysis(gene = rownames(deALL), simplify = TRUE) ## ----enrichment data, message=FALSE, warning=FALSE, eval=TRUE------------ data(enrichOutput) ## ----go bar, fig.height=8, fig.width=15.5, message=FALSE, warning=FALSE, eval=TRUE---- gdcEnrichPlot(enrichOutput, type = 'bar', category = 'GO', num.terms = 10) ## ----go bubble, echo=TRUE, fig.height=8, fig.width=12.5, message=FALSE, warning=FALSE, eval=TRUE---- gdcEnrichPlot(enrichOutput, type='bubble', category='GO', num.terms = 10) ## ----shiny pathview, message=FALSE, warning=FALSE, eval=FALSE------------ # library(pathview) # # deg <- deALL$logFC # names(deg) <- rownames(deALL) ## ----pathway, message=FALSE, warning=FALSE, eval=TRUE-------------------- pathways <- as.character(enrichOutput$Terms[enrichOutput$Category=='KEGG']) pathways ## ----shiny pathview2, eval=FALSE, echo=TRUE, message=FALSE, warning=FALSE---- # shinyPathview(deg, pathways = pathways, directory = 'pathview') ## ----sessionInfo--------------------------------------------------------- sessionInfo()