## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("BiocManager")) # install.packages("BiocManager") # BiocManager::install("RTCGAToolbox") ## ----------------------------------------------------------------------------- library(RTCGAToolbox) # Valid aliases getFirehoseDatasets() ## ----------------------------------------------------------------------------- # Valid stddata runs stddata <- getFirehoseRunningDates() stddata ## ----------------------------------------------------------------------------- # Valid analysis running dates (will return 3 recent date) gisticDate <- getFirehoseAnalyzeDates(last=3) gisticDate ## ----eval=TRUE,message=FALSE-------------------------------------------------- # READ mutation data and clinical data brcaData <- getFirehoseData(dataset="READ", runDate="20150402", forceDownload=TRUE, clinical=TRUE, Mutation=TRUE) ## ----------------------------------------------------------------------------- data(RTCGASample) RTCGASample ## ----------------------------------------------------------------------------- # Differential gene expression analysis for gene level RNA data. diffGeneExprs <- getDiffExpressedGenes(dataObject=RTCGASample, DrawPlots=TRUE, adj.method="BH", adj.pval=0.05, raw.pval=0.05, logFC=2, hmTopUpN=10, hmTopDownN=10) # Show head for expression outputs diffGeneExprs showResults(diffGeneExprs[[1]]) toptableOut <- showResults(diffGeneExprs[[1]]) ## ----------------------------------------------------------------------------- #Correlation between gene expression values and copy number corrGECN <- getCNGECorrelation(dataObject=RTCGASample, adj.method="BH", adj.pval=0.9, raw.pval=0.05) corrGECN showResults(corrGECN[[1]]) corRes <- showResults(corrGECN[[1]]) ## ----------------------------------------------------------------------------- # Mutation frequencies mutFrq <- getMutationRate(dataObject=RTCGASample) head(mutFrq[order(mutFrq[, 2], decreasing=TRUE), ]) ## ----fig.width=6,fig.height=6,fig.align='center'------------------------------ # Creating survival data frame and running analysis for # FCGBP which is one of the most frequently mutated gene in the toy data # Running following code will provide following KM plot. clinicData <- getData(RTCGASample,"clinical") head(clinicData) clinicData <- clinicData[, 3:5] clinicData[is.na(clinicData[, 3]), 3] <- clinicData[is.na(clinicData[, 3]), 2] survData <- data.frame(Samples=rownames(clinicData), Time=as.numeric(clinicData[, 3]), Censor=as.numeric(clinicData[, 1])) getSurvival(dataObject=RTCGASample, geneSymbols=c("FCGBP"), sampleTimeCensor=survData) ## ----------------------------------------------------------------------------- # Note: This function is provided for real dataset test since the toy dataset is small. RTCGASample ## ----message=FALSE------------------------------------------------------------ RTCGASampleClinical <- getData(RTCGASample, "clinical") RTCGASampleRNAseqCounts <- getData(RTCGASample, "RNASeqGene") RTCGASampleCN <- getData(RTCGASample, "GISTIC", "AllByGene") ## ----eval=FALSE--------------------------------------------------------------- # # BRCA data with mRNA (Both array and RNASeq), GISTIC processed copy number data # # mutation data and clinical data # # (Depends on bandwidth this process may take long time) # brcaData <- getFirehoseData (dataset="BRCA", runDate="20140416", # gistic2Date="20140115", clinic=TRUE, RNAseqGene=TRUE, mRNAArray=TRUE, # Mutation=TRUE) # # # Differential gene expression analysis for gene level RNA data. # # Heatmaps are given below. # diffGeneExprs <- getDiffExpressedGenes(dataObject=brcaData,DrawPlots=TRUE, # adj.method="BH", adj.pval=0.05, raw.pval=0.05, logFC=2, hmTopUpN=100, # hmTopDownN=100) # # Show head for expression outputs # diffGeneExprs # showResults(diffGeneExprs[[1]]) # toptableOut <- showResults(diffGeneExprs[[1]]) # # # Correlation between expresiion profiles and copy number data # corrGECN <- getCNGECorrelation(dataObject=brcaData, adj.method="BH", # adj.pval=0.05, raw.pval=0.05) # # corrGECN # showResults(corrGECN[[1]]) # corRes <- showResults(corrGECN[[1]]) # # # Gene mutation frequincies in BRCA dataset # mutFrq <- getMutationRate(dataObject=brcaData) # head(mutFrq[order(mutFrq[,2],decreasing=TRUE),]) # # # PIK3CA which is one of the most frequently mutated gene in BRCA dataset # # KM plot is given below. # clinicData <- getData(brcaData,"clinical") # head(clinicData) # clinicData <- clinicData[, 3:5] # clinicData[is.na(clinicData[, 3]), 3] <- clinicData[is.na(clinicData[, 3]), 2] # survData <- data.frame(Samples=rownames(clinicData), # Time=as.numeric(clinicData[, 3]), Censor=as.numeric(clinicData[, 1])) # getSurvival(dataObject=brcaData, geneSymbols=c("PIK3CA"), # sampleTimeCensor=survData) ## ----eval=FALSE--------------------------------------------------------------- # # Creating dataset analysis summary figure with getReport. # # Figure will be saved as PDF file. # library("Homo.sapiens") # locations <- genes(Homo.sapiens, columns="SYMBOL") # locations <- as.data.frame(locations) # locations <- locations[,c(6,1,5,2:3)] # locations <- locations[!is.na(locations[,1]), ] # locations <- locations[!duplicated(locations[,1]), ] # rownames(locations) <- locations[,1] # getReport(dataObject=brcaData, DGEResult1=diffGeneExprs[[1]], # DGEResult2=diffGeneExprs[[2]], geneLocations=locations) ## ----------------------------------------------------------------------------- data(RTCGASample) ## ----------------------------------------------------------------------------- sessionInfo()