## ----eval=FALSE---------------------------------------------------------- # source("http://bioconductor.org/biocLite.R") # biocLite("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, Clinic=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") ## ----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", gistic2_Date="20140115", # Clinic=TRUE, RNAseq_Gene=TRUE, mRNA_Array=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()