## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(dpi = 72) knitr::opts_chunk$set(cache=FALSE) ## ----fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("Moonlight2_pipeline_upd.png") ## ----eval = FALSE------------------------------------------------------------- # if (!require("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("Moonlight2R") ## ----eval = FALSE------------------------------------------------------------- # install.packages("devtools") # library(devtools) ## ----eval = FALSE------------------------------------------------------------- # devtools::install_github(repo = "ELELAB/Moonlight2R") ## ----eval = FALSE------------------------------------------------------------- # if (!require("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("BiocStyle") ## ----eval = FALSE------------------------------------------------------------- # devtools::install_github(repo = "ELELAB/Moonlight2R", build_vignettes = TRUE) ## ----eval = FALSE------------------------------------------------------------- # vignette( "Moonlight2R", package="Moonlight2R") ## ----eval = TRUE-------------------------------------------------------------- library(Moonlight2R) library(magrittr) library(dplyr) ## ----eval = TRUE-------------------------------------------------------------- data(DEGsmatrix) data(dataFilt) data(dataMAF) data(GEO_TCGAtab) data(LOC_transcription) data(LOC_translation) data(LOC_protein) data(Oncogenic_mediators_mutation_summary) data(DEG_Mutations_Annotations) data(dataMethyl) data(DEG_Methylation_Annotations) data(Oncogenic_mediators_methylation_summary) data(MetEvidenceDriver) data(LUAD_sample_anno) ## ----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 = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- dataFilt_GEO <- getDataGEO(GEOobject = "GSE20347", platform = "GPL571") ## ----eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- dataFilt_GEO <- getDataGEO(TCGAtumor = "ESCA") ## ----eval = TRUE, echo = TRUE, results='hide'--------------------------------- data(DEGsmatrix) data(DiseaseList) data(EAGenes) dataFEA <- FEA(DiffMatrix = DEGsmatrix, method="ora") ## ----eval = TRUE, echo = TRUE, message=FALSE, results='hide', warning=FALSE---- plotFEA(dataFEA = dataFEA, additionalFilename = "_exampleVignette", height = 10, width = 20) ## ----fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("FEAplot.gif") ## ----eval = TRUE-------------------------------------------------------------- data(DEGsmatrix) data(dataFilt) dataGRN <- GRN(DEGsmatrix = DEGsmatrix, TFs = sample(rownames(DEGsmatrix), 100), normCounts = dataFilt, nGenesPerm = 5, kNearest = 3, nBoot = 5, DiffGenes = TRUE) ## ----eval = TRUE, echo = TRUE, results='hide'--------------------------------- data(dataGRN) data(DEGsmatrix) data(DiseaseList) data(EAGenes) dataFEA <- FEA(DiffMatrix = DEGsmatrix) BPselected <- dataFEA$Diseases.or.Functions.Annotation[1:5] dataURA <- URA(dataGRN = dataGRN, DEGsmatrix = DEGsmatrix, BPname = BPselected, nCores=1) ## ----eval = TRUE-------------------------------------------------------------- data(dataURA) data(tabGrowBlock) data(knownDriverGenes) dataPRA <- PRA(dataURA = dataURA, BPname = c("apoptosis","proliferation of cells"), thres.role = 0) ## ----eval = FALSE------------------------------------------------------------- # data(dataPRA) # data(dataMAF) # data(DEGsmatrix) # data(LOC_transcription) # data(LOC_translation) # data(LOC_protein) # data(NCG) # data(EncodePromoters) # dataDMA <- DMA(dataMAF = dataMAF, # dataDEGs = DEGsmatrix, # dataPRA = dataPRA, # results_folder = "DMAresults", # coding_file = "css_coding.vcf.gz", # noncoding_file = "css_noncoding.vcf.gz") ## ----eval = TRUE-------------------------------------------------------------- data("dataMethyl") data("dataFilt") data("dataPRA") data("DEGsmatrix") data("LUAD_sample_anno") data("NCG") data("EncodePromoters") data("MetEvidenceDriver") # Subset column names (sample names) in expression data to patient level pattern <- "^(.{4}-.{2}-.{4}-.{2}).*" colnames(dataFilt) <- sub(pattern, "\\1", colnames(dataFilt)) dataGMA <- GMA(dataMET = dataMethyl, dataEXP = dataFilt, dataPRA = dataPRA, dataDEGs = DEGsmatrix, sample_info = LUAD_sample_anno, met_platform = "HM450", prevalence_filter = NULL, output_dir = "./GMAresults", cores = 1, roadmap.epigenome.ids = "E096", roadmap.epigenome.groups = NULL) ## ----eval = TRUE-------------------------------------------------------------- genes_query <- "BRCA1" dataGLS <- GLS(genes = genes_query, query_string = "AND cancer AND driver") head(dataGLS) ## ----eval = TRUE-------------------------------------------------------------- mavisp_db_location <- system.file('extdata', 'mavisp_db', package='Moonlight2R') specific_protein <- loadMAVISp(mavispDB = mavisp_db_location, mode = 'simple', proteins_of_interest = c('RUNX1')) all_proteins <- loadMAVISp(mavispDB = mavisp_db_location, mode = 'simple') ensemble <- loadMAVISp(mavispDB = mavisp_db_location, mode = 'ensemble', ensemble = 'cabsflex') ## ----eval = TRUE-------------------------------------------------------------- data(dataPRA) data(DEGsmatrix) data(dataTRRUST) data(dataMAF) data(dataMAVISp) TFresults <- TFinfluence(dataTRRUST = dataTRRUST, dataMAF = dataMAF, dataDEGs = DEGsmatrix, dataPRA = dataPRA, dataMAVISp = dataMAVISp, stabClassMAVISp = 'rasp') ## ----------------------------------------------------------------------------- knitr::kable(LOC_transcription) ## ----------------------------------------------------------------------------- knitr::kable(LOC_translation) ## ----------------------------------------------------------------------------- knitr::kable(LOC_protein) ## ----eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- data(knownDriverGenes) data(dataGRN) plotNetworkHive(dataGRN, knownDriverGenes, 0.55) ## ----eval = TRUE, warning = FALSE, message = FALSE, include=TRUE-------------- data(dataDMA) data(DEG_Mutations_Annotations) data(Oncogenic_mediators_mutation_summary) plotDMA(DEG_Mutations_Annotations, Oncogenic_mediators_mutation_summary, type = 'complete', additionalFilename = "") ## ----fig.width=3, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("heatmap_complete.gif") ## ----eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- data(DEG_Mutations_Annotations) data(Oncogenic_mediators_mutation_summary) data(dataURA_plot) plotMoonlight(DEG_Mutations_Annotations, Oncogenic_mediators_mutation_summary, dataURA_plot, gene_type = "drivers", n = 50) ## ----fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("moonlight_heatmap.gif") ## ----eval = TRUE, warning = FALSE, message = FALSE, include = TRUE------------ data("DEG_Methylation_Annotations") data("Oncogenic_mediators_methylation_summary") genes <- c("ACAN", "ACE2", "ADAM19", "AFAP1L1") plotGMA(DEG_Methylation_Annotations = DEG_Methylation_Annotations, Oncogenic_mediators_methylation_summary = Oncogenic_mediators_methylation_summary, type = "genelist", genelist = genes, additionalFilename = "./GMAresults/") ## ----fig.width=3, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("heatmap_genelist_met.gif") ## ----eval = TRUE, warning = FALSE, message = FALSE, include = TRUE------------ data("DEG_Methylation_Annotations") data("Oncogenic_mediators_methylation_summary") data("dataURA_plot") genes <- c("ACAN", "ACE2", "ADAM19", "AFAP1L1") plotMoonlightMet(DEG_Methylation_Annotations = DEG_Methylation_Annotations, Oncogenic_mediators_methylation_summary = Oncogenic_mediators_methylation_summary, dataURA = dataURA_plot, genes = genes, additionalFilename = "./GMAresults/") ## ----fig.width=3, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("moonlight_heatmap_met.gif") ## ----eval = TRUE, warning = FALSE, message = FALSE, include = TRUE------------ data("EpiMix_Results_Regular") data("dataMethyl") data("dataFilt") # Subset column names (sample names) in expression data to patient level pattern <- "^(.{4}-.{2}-.{4}-.{2}).*" colnames(dataFilt) <- sub(pattern, "\\1", colnames(dataFilt)) plotMetExp(EpiMix_results = EpiMix_Results_Regular, probe_name = "cg03035704", dataMET = dataMethyl, dataEXP = dataFilt, gene_of_interest = "ACVRL1", additionalFilename = "./GMAresults/") ## ----fig.width=3, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("plotMetExp.png") ## ----eval = TRUE,echo=TRUE,message=FALSE,warning=FALSE, results='hide'-------- data(DEGsmatrix) data(dataFilt) data(DiseaseList) data(EAGenes) data(tabGrowBlock) data(knownDriverGenes) dataFEA <- FEA(DiffMatrix = DEGsmatrix) dataGRN <- GRN(TFs = sample(rownames(DEGsmatrix), 100), DEGsmatrix = DEGsmatrix, DiffGenes = TRUE, normCounts = dataFilt, nGenesPerm = 5, nBoot = 5, kNearest = 3) dataURA <- URA(dataGRN = dataGRN, DEGsmatrix = DEGsmatrix, BPname = c("apoptosis", "proliferation of cells")) dataDual <- PRA(dataURA = dataURA, BPname = c("apoptosis", "proliferation of cells"), thres.role = 0) oncogenic_mediators <- list("TSG"=names(dataDual$TSG), "OCG"=names(dataDual$OCG)) ## ----eval = TRUE,message=FALSE,warning=FALSE, results='hide'------------------ data(dataURA) plotURA(dataURA = dataURA, additionalFilename = "_exampleVignette") ## ----fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("URAplot.gif") ## ----eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE----------------------- # data(dataFilt) # data(DEGsmatrix) # data(dataMAF) # data(DiseaseList) # data(EAGenes) # data(tabGrowBlock) # data(knownDriverGenes) # data(LOC_transcription) # data(LOC_translation) # data(LOC_protein) # data(NCG) # data(EncodePromoters) # # listMoonlight <- moonlight(dataDEGs = DEGsmatrix, # dataFilt = dataFilt, # nTF = 100, # DiffGenes = TRUE, # nGenesPerm = 5, # nBoot = 5, # BPname = c("apoptosis","proliferation of cells"), # dataMAF = dataMAF, # path_cscape_coding = "css_coding.vcf.gz", # path_cscape_noncoding = "css_noncoding.vcf.gz") # save(listMoonlight, file = paste0("listMoonlight_ncancer4.Rdata")) # # ## ----eval = TRUE, echo = TRUE, results='hide', warning = FALSE, message = FALSE---- data(listMoonlight) plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5") ## ----fig.width=3, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE---- knitr::include_graphics("circos_ocg_tsg_ncancer5.gif") ## ----eval = FALSE,echo=TRUE,message=FALSE,warning=FALSE----------------------- # # data(DEGsmatrix) # data(dataFilt) # data(dataMAF) # data(DiseaseList) # data(EAGenes) # data(tabGrowBlock) # data(knownDriverGenes) # data(LOC_transcription) # data(LOC_translation) # data(LOC_protein) # data(NCG) # data(EncodePromoters) # # # Perform gene regulatory network analysis # dataGRN <- GRN(TFs = rownames(DEGsmatrix), # DEGsmatrix = DEGsmatrix, # DiffGenes = TRUE, # normCounts = dataFilt, # nGenesPerm = 5, # kNearest = 3, # nBoot = 5) # # # Perform upstream regulatory analysis # # As example, we use apoptosis and proliferation of cells as the biological processes # dataURA <- URA(dataGRN = dataGRN, # DEGsmatrix = DEGsmatrix, # BPname = c("apoptosis", # "proliferation of cells"), # nCores = 1) # # # Perform pattern recognition analysis # dataPRA <- PRA(dataURA = dataURA, # BPname = c("apoptosis", # "proliferation of cells"), # thres.role = 0) # # # Perform driver mutation analysis # dataDMA <- DMA(dataMAF = dataMAF, # dataDEGs = DEGsmatrix, # dataPRA = dataPRA, # results_folder = "DMAresults", # coding_file = "css_coding.vcf.gz", # noncoding_file = "css_noncoding.vcf.gz") # ## ----eval = TRUE-------------------------------------------------------------- data(Oncogenic_mediators_mutation_summary) data(DEG_Mutations_Annotations) # Extract oncogenic mediators that contain at least one driver mutation # These are the driver genes knitr::kable(Oncogenic_mediators_mutation_summary %>% filter(!is.na(CScape_Driver))) # Extract mutation annotations of the predicted driver genes driver_mut <- DEG_Mutations_Annotations %>% filter(!is.na(Moonlight_Oncogenic_Mediator), CScape_Mut_Class == "Driver") # Extract driver genes with a predicted effect on the transcriptional level transcription_mut <- Oncogenic_mediators_mutation_summary %>% filter(!is.na(CScape_Driver)) %>% filter(Transcription_mut_sum > 0) # Extract mutation annotations of predicted driver genes that have a driver mutation # in its promoter region with a potential effect on the transcriptional level promoters <- DEG_Mutations_Annotations %>% filter(!is.na(Moonlight_Oncogenic_Mediator), CScape_Mut_Class == "Driver", Potential_Effect_on_Transcription == 1, Annotation == 'Promoter') ## ----eval = TRUE,echo=TRUE,message=FALSE,warning=FALSE,results='hide'--------- data(DEGsmatrix) data(dataFilt) data(dataMAF) data(DiseaseList) data(EAGenes) data(tabGrowBlock) data(knownDriverGenes) data(LOC_transcription) data(LOC_translation) data(LOC_protein) data(NCG) data(EncodePromoters) data(dataMethyl) data(LUAD_sample_anno) data(MetEvidenceDriver) # Perform gene regulatory network analysis dataGRN <- GRN(TFs = rownames(DEGsmatrix), DEGsmatrix = DEGsmatrix, DiffGenes = TRUE, normCounts = dataFilt, nGenesPerm = 5, kNearest = 3, nBoot = 5) # Perform upstream regulatory analysis # As example, we use apoptosis and proliferation of cells as the biological processes dataURA <- URA(dataGRN = dataGRN, DEGsmatrix = DEGsmatrix, BPname = c("apoptosis", "proliferation of cells"), nCores = 1) # Perform pattern recognition analysis dataPRA <- PRA(dataURA = dataURA, BPname = c("apoptosis", "proliferation of cells"), thres.role = 0) # Subset column names (sample names) in expression data to patient level pattern <- "^(.{4}-.{2}-.{4}-.{2}).*" colnames(dataFilt) <- sub(pattern, "\\1", colnames(dataFilt)) # Perform gene methylation analysis dataGMA <- GMA(dataMET = dataMethyl, dataEXP = dataFilt, dataPRA = dataPRA, dataDEGs = DEGsmatrix, sample_info = LUAD_sample_anno, met_platform = "HM450", prevalence_filter = NULL, output_dir = "./GMAresults", cores = 1, roadmap.epigenome.ids = "E096", roadmap.epigenome.groups = NULL) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()