## ----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(".") ## ---- fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- library(png) library(grid) img <- readPNG("Moonlight_Pipeline.png") grid.raster(img) ## ---- eval = FALSE------------------------------------------------------------ # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install("MoonlightR") ## ---- eval = FALSE------------------------------------------------------------ # dataFilt <- getDataTCGA(cancerType = "LUAD", # dataType = "Gene expression", # directory = "data", # nSample = 4) ## ---- eval = FALSE------------------------------------------------------------ # dataFilt <- getDataTCGA(cancerType = "BRCA", # dataType = "Methylation", # directory = "data",nSample = 4) ## ---- eval = TRUE, echo = TRUE------------------------------------------------ knitr::kable(GEO_TCGAtab, digits = 2, caption = "Table with GEO data set matched to one of the 18 given TCGA cancer types ", row.names = TRUE) ## ---- eval = FALSE , echo = TRUE, results='hide', warning = FALSE, message = FALSE---- # dataFilt <- getDataGEO(GEOobject = "GSE20347",platform = "GPL571") ## ---- eval = FALSE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- # dataFilt <- getDataGEO(TCGAtumor = "ESCA") ## ---- eval = FALSE, message=FALSE, results='hide', warning=FALSE-------------- # dataDEGs <- DPA(dataFilt = dataFilt, # dataType = "Gene expression") ## ---- eval = FALSE, echo = TRUE, hide=TRUE, results='hide', warning = FALSE, message = FALSE---- # data(GEO_TCGAtab) # DataAnalysisGEO<- "../GEO_dataset/" # i<-5 # # cancer <- GEO_TCGAtab$Cancer[i] # cancerGEO <- GEO_TCGAtab$Dataset[i] # cancerPLT <-GEO_TCGAtab$Platform[i] # fileCancerGEO <- paste0(cancer,"_GEO_",cancerGEO,"_",cancerPLT, ".RData") # # dataFilt <- getDataGEO(TCGAtumor = cancer) # xContrast <- c("G1-G0") # GEOdegs <- DPA(dataConsortium = "GEO", # gset = dataFilt , # colDescription = "title", # samplesType = c(GEO_TCGAtab$GEO_Normal[i], # GEO_TCGAtab$GEO_Tumor[i]), # fdr.cut = 0.01, # logFC.cut = 1, # gsetFile = paste0(DataAnalysisGEO,fileCancerGEO)) ## ---- eval = TRUE, echo = TRUE------------------------------------------------ library(TCGAbiolinks) TCGAVisualize_volcano(DEGsmatrix$logFC, DEGsmatrix$FDR, filename = "DEGs_volcano.png", x.cut = 1, y.cut = 0.05, names = rownames(DEGsmatrix), color = c("black","red","dodgerblue3"), names.size = 2, show.names = "highlighted", highlight = c("gene1","gene2"), xlab = " Gene expression fold change (Log2)", legend = "State", title = "Volcano plot (Normal NT vs Tumor TP)", width = 10) ## ---- fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- img <- readPNG("DEGs_volcano.png") grid.raster(img) ## ---- eval = TRUE, echo = TRUE, results='hide'-------------------------------- data(DEGsmatrix) dataFEA <- FEA(DEGsmatrix = DEGsmatrix) ## ---- eval = TRUE, echo = TRUE, message=FALSE, results='hide', warning=FALSE---- plotFEA(dataFEA = dataFEA, additionalFilename = "_exampleVignette", height = 20, width = 10) ## ---- fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- img <- readPNG("FEAplot.png") grid.raster(img) ## ---- eval = TRUE------------------------------------------------------------- dataGRN <- GRN(TFs = rownames(DEGsmatrix)[1:100], normCounts = dataFilt, nGenesPerm = 10,kNearest = 3,nBoot = 10) ## ---- eval = FALSE, echo = TRUE, results='hide'------------------------------- # data(dataGRN) # data(DEGsmatrix) # # dataFEA <- FEA(DEGsmatrix = DEGsmatrix) # # BPselected <- dataFEA$Diseases.or.Functions.Annotation[1:5] # dataURA <- URA(dataGRN = dataGRN, # DEGsmatrix = DEGsmatrix, # BPname = BPselected, # nCores=1) ## ---- eval = TRUE------------------------------------------------------------- data(dataURA) dataDual <- PRA(dataURA = dataURA, BPname = c("apoptosis","proliferation of cells"), thres.role = 0) ## ---- eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- data(knownDriverGenes) data(dataGRN) plotNetworkHive(dataGRN, knownDriverGenes, 0.55) ## ----eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE, results='hide'------- # dataDEGs <- DPA(dataFilt = dataFilt, # dataType = "Gene expression") # # dataFEA <- FEA(DEGsmatrix = dataDEGs) # # dataGRN <- GRN(TFs = rownames(dataDEGs)[1:100], # DEGsmatrix = dataDEGs, # DiffGenes = TRUE, # normCounts = dataFilt) # # dataURA <- URA(dataGRN = dataGRN, # DEGsmatrix = dataDEGs, # BPname = c("apoptosis", # "proliferation of cells")) # # dataDual <- PRA(dataURA = dataURA, # BPname = c("apoptosis", # "proliferation of cells"), # thres.role = 0) # # CancerGenes <- list("TSG"=names(dataDual$TSG), "OCG"=names(dataDual$OCG)) # ## ---- eval = TRUE,message=FALSE,warning=FALSE, results='hide'----------------- plotURA(dataURA = dataURA[c(names(dataDual$TSG), names(dataDual$OCG)),, drop = FALSE], additionalFilename = "_exampleVignette") ## ---- fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- img <- readPNG("URAplot.png") grid.raster(img) ## ----eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE----------------------- # cancerList <- c("BLCA","COAD","ESCA","HNSC","STAD") # # listMoonlight <- moonlight(cancerType = cancerList, # dataType = "Gene expression", # directory = "data", # nSample = 10, # nTF = 100, # DiffGenes = TRUE, # BPname = c("apoptosis","proliferation of cells")) # save(listMoonlight, file = paste0("listMoonlight_ncancer4.Rdata")) # ## ---- eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5") ## ---- fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- img <- readPNG("circos_ocg_tsg_ncancer5.png") grid.raster(img) ## ----eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE----------------------- # # listMoonlight <- NULL # for (i in 1:4){ # dataDual <- moonlight(cancerType = "BRCA", # dataType = "Gene expression", # directory = "data", # nSample = 10, # nTF = 5, # DiffGenes = TRUE, # BPname = c("apoptosis","proliferation of cells"), # stage = i) # listMoonlight <- c(listMoonlight, list(dataDual)) # save(dataDual, file = paste0("dataDual_stage",as.roman(i), ".Rdata")) # } # names(listMoonlight) <- c("stage1", "stage2", "stage3", "stage4") # # # Prepare mutation data for stages # # mutation <- GDCquery_Maf(tumor = "BRCA") # # res.mutation <- NULL # for(stage in 1:4){ # # curStage <- paste0("Stage ", as.roman(stage)) # dataClin$tumor_stage <- toupper(dataClin$tumor_stage) # dataClin$tumor_stage <- gsub("[ABCDEFGH]","",dataClin$tumor_stage) # dataClin$tumor_stage <- gsub("ST","Stage",dataClin$tumor_stage) # # dataStg <- dataClin[dataClin$tumor_stage %in% curStage,] # message(paste(curStage, "with", nrow(dataStg), "samples")) # dataSmTP <- mutation$Tumor_Sample_Barcode # # dataStgC <- dataSmTP[substr(dataSmTP,1,12) %in% dataStg$bcr_patient_barcode] # dataSmTP <- dataStgC # # info.mutation <- mutation[mutation$Tumor_Sample_Barcode %in% dataSmTP,] # # ind <- which(info.mutation[,"Consequence"]=="inframe_deletion") # ind2 <- which(info.mutation[,"Consequence"]=="inframe_insertion") # ind3 <- which(info.mutation[,"Consequence"]=="missense_variant") # res.mutation <- c(res.mutation, list(info.mutation[c(ind, ind2, ind3),c(1,51)])) # } # names(res.mutation) <- c("stage1", "stage2", "stage3", "stage4") # # # tmp <- NULL # tmp <- c(tmp, list(listMoonlight[[1]][[1]])) # tmp <- c(tmp, list(listMoonlight[[2]][[1]])) # tmp <- c(tmp, list(listMoonlight[[3]][[1]])) # tmp <- c(tmp, list(listMoonlight[[4]][[1]])) # names(tmp) <- names(listMoonlight) # # mutation <- GDCquery_Maf(tumor = "BRCA") # # plotCircos(listMoonlight=listMoonlight,listMutation=res.mutation, additionalFilename="proc2_wmutation", intensityColDual=0.2,fontSize = 2) ## ---- fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- img <- readPNG("circos_ocg_tsg_stages.png") grid.raster(img) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()