## ----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-------------------------------------------------------
#  source("https://bioconductor.org/biocLite.R")
#  biocLite("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$Platform[i]
#  fileCancerGEO <- paste0(cancer,"_GEO_",cancerGEO,"_",cancerPLT, ".RData")
#  
#  dataFilt <- getDataGEO(TCGAtumor = cancer)
#  
#  GEOdegs <- DPA(dataConsortium = "GEO",
#                 gset = dataFilt ,
#                 colDescription = "title",
#                 samplesType  = c(GEO_TCGAtab$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 = 7,
                      y.cut = 10^-5,
                      names = rownames(DEGsmatrix),
                      color = c("black","red","dodgerblue3"),
                      names.size = 2,
                      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)

BPselected <- "apoptosis"

BPannotations <- DiseaseList[[match(BPselected,names(DiseaseList))]]$ID
dataLPA <- LPA(dataDEGs = DEGsmatrix[1:5,],
               BP =  BPselected,
               BPlist = BPannotations)
DiseaseListNew <- dataLPA
names(DiseaseListNew) <- BPselected


## ---- 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 = TRUE, echo = TRUE, results='hide'---------------------------
data(dataGRN)
data(DEGsmatrix)
dataURA <- URA(dataGRN = dataGRN,
               DEGsmatrix = DEGsmatrix,
               BPname = NULL, nCores=2)

## ---- 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 = TRUE,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()