## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(dpi = 300) knitr::opts_chunk$set(cache=FALSE) ## ----echo = FALSE,hide=TRUE, message=FALSE,warning=FALSE---------------------- devtools::load_all(".") ## ----message=FALSE, warning=FALSE, include=FALSE------------------------------ library(SummarizedExperiment) library(dplyr) library(DT) ## ----eval = FALSE------------------------------------------------------------- # # You can define a list of samples to query and download providing relative TCGA barcodes. # listSamples <- c( # "TCGA-E9-A1NG-11A-52R-A14M-07","TCGA-BH-A1FC-11A-32R-A13Q-07", # "TCGA-A7-A13G-11A-51R-A13Q-07","TCGA-BH-A0DK-11A-13R-A089-07", # "TCGA-E9-A1RH-11A-34R-A169-07","TCGA-BH-A0AU-01A-11R-A12P-07", # "TCGA-C8-A1HJ-01A-11R-A13Q-07","TCGA-A7-A13D-01A-13R-A12P-07", # "TCGA-A2-A0CV-01A-31R-A115-07","TCGA-AQ-A0Y5-01A-11R-A14M-07" # ) # # # Query platform Illumina HiSeq with a list of barcode # query <- GDCquery( # project = "TCGA-BRCA", # data.category = "Transcriptome Profiling", # data.type = "Gene Expression Quantification", # barcode = listSamples # ) # # # Download a list of barcodes with platform IlluminaHiSeq_RNASeqV2 # GDCdownload(query) # # # Prepare expression matrix with geneID in the rows and samples (barcode) in the columns # # rsem.genes.results as values # BRCA.Rnaseq.SE <- GDCprepare(query) # # BRCAMatrix <- assay(BRCA.Rnaseq.SE,"unstranded") # # For gene expression if you need to see a boxplot correlation and AAIC plot to define outliers you can run # BRCA.RNAseq_CorOutliers <- TCGAanalyze_Preprocessing(BRCA.Rnaseq.SE) ## ----eval = TRUE, echo = FALSE,size = 8--------------------------------------- library(TCGAbiolinks) dataGE <- dataBRCA[sample(rownames(dataBRCA),10),sample(colnames(dataBRCA),7)] knitr::kable( dataGE[1:10,2:3], digits = 2, caption = "Example of a matrix of gene expression (10 genes in rows and 2 samples in columns)", row.names = TRUE ) ## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"---------------- library(png) library(grid) img <- readPNG("PreprocessingOutput.png") grid.raster(img) ## ----eval = FALSE------------------------------------------------------------- # library(TCGAbiolinks) # # # normalization of genes # dataNorm <- TCGAanalyze_Normalization( # tabDF = BRCA.RNAseq_CorOutliers, # geneInfo = geneInfoHT # ) # # # quantile filter of genes # dataFilt <- TCGAanalyze_Filtering( # tabDF = dataNorm, # method = "quantile", # qnt.cut = 0.25 # ) # # # selection of normal samples "NT" # samplesNT <- TCGAquery_SampleTypes( # barcode = colnames(dataFilt), # typesample = c("NT") # ) # # # selection of tumor samples "TP" # samplesTP <- TCGAquery_SampleTypes( # barcode = colnames(dataFilt), # typesample = c("TP") # ) # # # Diff.expr.analysis (DEA) # dataDEGs <- TCGAanalyze_DEA( # mat1 = dataFilt[,samplesNT], # mat2 = dataFilt[,samplesTP], # Cond1type = "Normal", # Cond2type = "Tumor", # fdr.cut = 0.01 , # logFC.cut = 1, # method = "glmLRT" # ) # # # DEGs table with expression values in normal and tumor samples # dataDEGsFiltLevel <- TCGAanalyze_LevelTab( # FC_FDR_table_mRNA = dataDEGs, # typeCond1 = "Tumor", # typeCond2 = "Normal", # TableCond1 = dataFilt[,samplesTP], # TableCond2 = dataFilt[,samplesNT] # ) # ## ----eval = TRUE, echo = FALSE------------------------------------------------ library(TCGAbiolinks) dataDEGsFiltLevel$FDR <- format(dataDEGsFiltLevel$FDR, scientific = TRUE) knitr::kable( dataDEGsFiltLevel[1:10,], digits = 2, caption = "Table of DEGs after DEA", row.names = FALSE ) ## ----eval=FALSE,echo=TRUE,message=FALSE,warning=FALSE------------------------- # # query <- GDCquery( # project = "TCGA-BRCA", # data.category = "Transcriptome Profiling", # data.type = "Gene Expression Quantification", # workflow.type = "STAR - Counts" # ) # # samplesDown <- getResults(query,cols=c("cases")) # # dataSmTP <- TCGAquery_SampleTypes( # barcode = samplesDown, # typesample = "TP" # ) # # dataSmNT <- TCGAquery_SampleTypes( # barcode = samplesDown, # typesample = "NT" # ) # dataSmTP_short <- dataSmTP[1:10] # dataSmNT_short <- dataSmNT[1:10] # # query.selected.samples <- GDCquery( # project = "TCGA-BRCA", # data.category = "Transcriptome Profiling", # data.type = "Gene Expression Quantification", # workflow.type = "STAR - Counts", # barcode = c(dataSmTP_short, dataSmNT_short) # ) # # GDCdownload( # query = query.selected.samples # ) # # dataPrep <- GDCprepare( # query = query.selected.samples, # save = TRUE # ) # # dataPrep <- TCGAanalyze_Preprocessing( # object = dataPrep, # cor.cut = 0.6, # datatype = "HTSeq - Counts" # ) # # dataNorm <- TCGAanalyze_Normalization( # tabDF = dataPrep, # geneInfo = geneInfoHT, # method = "gcContent" # ) # # boxplot(dataPrep, outline = FALSE) # # boxplot(dataNorm, outline = FALSE) # # dataFilt <- TCGAanalyze_Filtering( # tabDF = dataNorm, # method = "quantile", # qnt.cut = 0.25 # ) # # dataDEGs <- TCGAanalyze_DEA( # mat1 = dataFilt[,dataSmTP_short], # mat2 = dataFilt[,dataSmNT_short], # Cond1type = "Normal", # Cond2type = "Tumor", # fdr.cut = 0.01 , # logFC.cut = 1, # method = "glmLRT" # ) # ## ----eval=FALSE,echo=TRUE,message=FALSE,warning=FALSE------------------------- # require(TCGAbiolinks) # # query.miRNA <- GDCquery( # project = "TCGA-BRCA", # experimental.strategy = "miRNA-Seq", # data.category = "Transcriptome Profiling", # data.type = "miRNA Expression Quantification" # ) # # GDCdownload(query = query.miRNA) # # dataAssy.miR <- GDCprepare( # query = query.miRNA # ) # rownames(dataAssy.miR) <- dataAssy.miR$miRNA_ID # # # using read_count's data # read_countData <- colnames(dataAssy.miR)[grep("count", colnames(dataAssy.miR))] # dataAssy.miR <- dataAssy.miR[,read_countData] # colnames(dataAssy.miR) <- gsub("read_count_","", colnames(dataAssy.miR)) # # dataFilt <- TCGAanalyze_Filtering( # tabDF = dataAssy.miR, # method = "quantile", # qnt.cut = 0.25 # ) # # dataDEGs <- TCGAanalyze_DEA( # mat1 = dataFilt[,dataSmNT_short.miR], # mat2 = dataFilt[,dataSmTP_short.miR], # Cond1type = "Normal", # Cond2type = "Tumor", # fdr.cut = 0.01 , # logFC.cut = 1, # method = "glmLRT" # ) # ## ----eval = FALSE------------------------------------------------------------- # library(TCGAbiolinks) # # Enrichment Analysis EA # # Gene Ontology (GO) and Pathway enrichment by DEGs list # Genelist <- rownames(dataDEGsFiltLevel) # # ansEA <- TCGAanalyze_EAcomplete( # TFname = "DEA genes Normal Vs Tumor", # RegulonList = Genelist # ) # # # Enrichment Analysis EA (TCGAVisualize) # # Gene Ontology (GO) and Pathway enrichment barPlot # # TCGAvisualize_EAbarplot( # tf = rownames(ansEA$ResBP), # GOBPTab = ansEA$ResBP, # GOCCTab = ansEA$ResCC, # GOMFTab = ansEA$ResMF, # PathTab = ansEA$ResPat, # nRGTab = Genelist, # nBar = 10 # ) # ## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"---------------- library(png) library(grid) img <- readPNG("EAplot.png") grid.raster(img) ## ----eval = FALSE------------------------------------------------------------- # clin.gbm <- GDCquery_clinic("TCGA-GBM", "clinical") # TCGAanalyze_survival( # data = clin.gbm, # clusterCol = "gender", # main = "TCGA Set\n GBM", # height = 10, # width=10 # ) ## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- library(png) library(grid) img <- readPNG("case2_surv.png") grid.raster(img) ## ----eval = FALSE------------------------------------------------------------- # library(TCGAbiolinks) # # Survival Analysis SA # # clinical_patient_Cancer <- GDCquery_clinic("TCGA-BRCA","clinical") # dataBRCAcomplete <- log2(BRCA_rnaseqv2) # # tokenStop <- 1 # # tabSurvKMcomplete <- NULL # # for( i in 1: round(nrow(dataBRCAcomplete)/100)){ # message( paste( i, "of ", round(nrow(dataBRCAcomplete)/100))) # tokenStart <- tokenStop # tokenStop <- 100 * i # tabSurvKM <- TCGAanalyze_SurvivalKM( # clinical_patient_Cancer, # dataBRCAcomplete, # Genelist = rownames(dataBRCAcomplete)[tokenStart:tokenStop], # Survresult = F, # ThreshTop = 0.67, # ThreshDown = 0.33 # ) # # tabSurvKMcomplete <- rbind(tabSurvKMcomplete,tabSurvKM) # } # # tabSurvKMcomplete <- tabSurvKMcomplete[tabSurvKMcomplete$pvalue < 0.01,] # tabSurvKMcomplete <- tabSurvKMcomplete[order(tabSurvKMcomplete$pvalue, decreasing=F),] # # tabSurvKMcompleteDEGs <- tabSurvKMcomplete[ # rownames(tabSurvKMcomplete) %in% dataDEGsFiltLevel$mRNA, # ] ## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"---------------- tabSurvKMcompleteDEGs$pvalue <- format(tabSurvKMcompleteDEGs$pvalue, scientific = TRUE) knitr::kable(tabSurvKMcompleteDEGs[1:5,1:4], digits = 2, caption = "Table KM-survival genes after SA", row.names = TRUE) knitr::kable(tabSurvKMcompleteDEGs[1:5,5:7], digits = 2, row.names = TRUE) ## ----eval = FALSE------------------------------------------------------------- # data <- TCGAanalyze_DMC( # data = data, # groupCol = "methylation_subtype", # group1 = "CIMP.H", # group2 = "CIMP.L", # p.cut = 10^-5, # diffmean.cut = 0.25, # legend = "State", # plot.filename = "coad_CIMPHvsCIMPL_metvolcano.png" # ) ## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- library(png) library(grid) img <- readPNG("figure5met.png") grid.raster(img) ## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- library(jpeg) library(grid) img <- readJPEG("case2_Heatmap.jpg") grid.raster(img) ## ----eval = FALSE------------------------------------------------------------- # # normalization of genes # dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo) # # # quantile filter of genes # dataFilt <- TCGAanalyze_Filtering( # tabDF = dataNorm, # method = "quantile", # qnt.cut = 0.25 # ) # # # selection of normal samples "NT" # group1 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT")) # # selection of normal samples "TP" # group2 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP")) # # # Principal Component Analysis plot for ntop selected DEGs # pca <- TCGAvisualize_PCA( # dataFilt = dataFilt, # dataDEGsFiltLevel = dataDEGsFiltLevel, # ntopgenes = 200, # group1 = group1, # group2 = group2 # ) ## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"---------------- library(png) library(grid) img <- readPNG("PCAtop200DEGs.png") grid.raster(img) ## ----include=FALSE,echo=FALSE, fig.height=5, message=FALSE, warning=FALSE,eval=FALSE---- # query <- GDCquery( # project = "TCGA-GBM", # data.category = "DNA Methylation", # platform = "Illumina Human Methylation 27", # barcode = c( # "TCGA-02-0058-01A-01D-0186-05", "TCGA-12-1597-01B-01D-0915-05", # "TCGA-12-0829-01A-01D-0392-05", "TCGA-06-0155-01B-01D-0521-05", # "TCGA-02-0099-01A-01D-0199-05", "TCGA-19-4068-01A-01D-1228-05", # "TCGA-19-1788-01A-01D-0595-05", "TCGA-16-0848-01A-01D-0392-05" # ) # ) # GDCdownload(query, method = "api") # data <- GDCprepare(query) # ## ----eval=FALSE, echo=TRUE, fig.height=5, message=FALSE, warning=FALSE-------- # query <- GDCquery( # project = "TCGA-GBM", # data.category = "DNA methylation", # platform = "Illumina Human Methylation 27", # barcode = c( # "TCGA-02-0058-01A-01D-0186-05", "TCGA-12-1597-01B-01D-0915-05", # "TCGA-12-0829-01A-01D-0392-05", "TCGA-06-0155-01B-01D-0521-05", # "TCGA-02-0099-01A-01D-0199-05", "TCGA-19-4068-01A-01D-1228-05", # "TCGA-19-1788-01A-01D-0595-05", "TCGA-16-0848-01A-01D-0392-05" # ) # ) # GDCdownload(query, method = "api") # data <- GDCprepare(query) ## ----eval = FALSE------------------------------------------------------------- # starburst <- TCGAvisualize_starburst( # met = coad.SummarizeExperiment, # exp = different.experssion.analysis.data, # group1 = "CIMP.H", # group2 = "CIMP.L", # met.platform = "450K", # genome = "hg19", # met.p.cut = 10^-5, # exp.p.cut = 10^-5, # names = TRUE # ) ## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- library(png) library(grid) img <- readPNG("figure5star.png") grid.raster(img) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()