## ----style, echo=FALSE, results="asis", message=FALSE, warnings = FALSE------- knitr::opts_chunk$set(tidy = FALSE, warning = FALSE, message = FALSE,fig.width=6, fig.height=6 ) ## ----Install, eval=FALSE, echo=TRUE, include=TRUE----------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("NoRCE") ## ----load, eval=FALSE, echo=TRUE, include=TRUE-------------------------------- # library(NoRCE) ## ----change parameters, eval=FALSE, echo=TRUE, include=TRUE------------------- # type <- c('downstream','upstream') # # value <- c(2000,30000) # setParameters(type,value) # # #To change the single parameter # setParameters("pathwayType","reactome") ## ---- gene, eval=FALSE-------------------------------------------------------- # #Set the neighbourhood region as exon # ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', near=TRUE, genetype = 'Ensembl_gene') # ## ---- lysis, eval=FALSE------------------------------------------------------- # #Set the neighbourhood region as exon # setParameters("searchRegion", "exon") # # #NoRCE automatically consider only the exon of the genes # ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', near=TRUE, genetype = 'Ensembl_gene') # ## ---- region_go, eval=FALSE--------------------------------------------------- # #Change back to all search regions # setParameters("searchRegion", "all") # # #Import the gene set regions # regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE") # regionNC <- rtracklayer::import(regions, format = "BED") # # #Perform the analysis on the gene regions # regionGO<-geneRegionGOEnricher(region = regionNC, org_assembly= 'hg19', near = TRUE) # ## ---- Intersection of the nearest genes of the input gene set and the potential target set is carries out for enrichment analysis, eval=FALSE---- # mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, target=TRUE) # ## ---- Enrichment based on TAD cellline, eval=FALSE---------------------------- # mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, isTADSearch = TRUE, TAD = tad_hg19) # ## ---- eval=FALSE-------------------------------------------------------------- # # Read the custom TAD boundaries # cus_TAD<-system.file("extdata", "DER-18_TAD_adultbrain.txt", package = "NoRCE") # tad_custom <- rtracklayer::import(cus_TAD, format = 'bed') # # # Use custom TAD boundaries for enrichment # ncGO_tad <- geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', genetype = 'Ensembl_gene', isTADSearch = TRUE, TAD = tad_custom) # ## ---- Retrieve list of cell-line , eval=FALSE--------------------------------- # a<-listTAD(TADName = tad_hg19) ## ---- corr tcga, eval=FALSE--------------------------------------------------- # ncGO_tad <- geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', genetype = 'Ensembl_gene',near = TRUE, express = TRUE, databaseFile = '\\miRCancer\\miRCancer.db', cancer = 'GBMLGG') # ## ---- corr analiz, eval=FALSE------------------------------------------------- # # # miRNA targets and custom RNAseq expression of miRNA and mRNA are used # miGO1 <- mirnaGOEnricher(gene = brain_mirna, org_assembly = 'hg19', target = TRUE, express = TRUE, isCustomExp = TRUE, exp1 = mirna, exp2 = mrna) # ## ---- corr union analiz, eval=FALSE------------------------------------------- # # # Union of miRNA targets and custom RNAseq expression of miRNA and mRNA is utilized for enrichment analysis # miGO1 <- mirnaGOEnricher(gene = brain_mirna, org_assembly = 'hg19', target = TRUE, express = TRUE, isCustomExp = TRUE, exp1 = mirna, exp2 = mrna, isUnionCorGene = TRUE) ## ---- pathway biotype , eval=FALSE-------------------------------------------- # # KEGG enrichment is performed # miPathway <- mirnaPathwayEnricher(gene = brain_mirna, org_assembly = 'hg19', near = TRUE, target = TRUE) ## ---- reactome pathway, eval=FALSE-------------------------------------------- # # Change the pathway database # setParameters("pathwayType","reactome") # # nc2 <- genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', near = TRUE, genetype = 'Ensembl_gene') # # # Wiki pathway Enrichment # # # Change the pathway database type and multiple testing correction method # type <- c('pathwayType', 'pAdjust') # value<-c('wiki', 'bonferroni') # setParameters(type,value) # nc2 <- genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', near = TRUE, genetype = 'Ensembl_gene') ## ---- custom pathway , eval=FALSE--------------------------------------------- # setParameters("pathwayType","other") # # # Name of the gmt file in the local # wp.gmt <- "custom.gmt" # # # GMT file should be on the working directory # ncGO_bader <- genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19',genetype = 'Ensembl_gene',gmtName = wp.gmt) ## ---- extract biotype , eval=FALSE-------------------------------------------- # biotypes <- c('unprocessed_pseudogene','transcribed_unprocessed_pseudogene') # # #Temp.gft is a subset of GENCODE Long non-coding RNA gene annotation # fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE") # # extrResult <- filterBiotype(fileImport, biotypes) ## ---- biotype process , eval=FALSE-------------------------------------------- # #Extract biotype gene interactions from the given GTF formatted file # fileImport<-system.file("extdata", "temp.gtf", package = "NoRCE") # # #Creates 2 dimensional input Gene-Biotype matrix # gtf <- extractBiotype(gtfFile = fileImport) ## ---- tabular, eval=FALSE----------------------------------------------------- # writeEnrichment(mrnaObject = ncGO,fileName = "result.txt",sept = "\t",type = "pvalue") # # writeEnrichment(mrnaObject = ncGO,fileName = "result.txt",sept = "\t",type = "pvalue", n = 5) ## ---- dot, eval=FALSE--------------------------------------------------------- # drawDotPlot(mrnaObject = ncGO, type = "pvalue", n = 25) ## ---- network, eval=FALSE----------------------------------------------------- # createNetwork(mrnaObject = ncGO, type = 'pvalue', n = 2, isNonCode = TRUE) ## ---- dag, eval=FALSE--------------------------------------------------------- # getGoDag(mrnaObject = ncGO,type = 'pvalue', n = 2,filename = 'dag.png', imageFormat = "png") ## ---- kegg diagram, eval=FALSE------------------------------------------------ # getKeggDiagram(mrnaObject = ncRNAPathway, org_assembly ='hg19', pathway = ncRNAPathway@ID[1]) ## ---- reactome diagram, eval=FALSE-------------------------------------------- # getReactomeDiagram(mrnaObject = ncRNAPathway, pathway = miGO1@ID[3], imageFormat = "png") ## ---- targetmi, eval=FALSE---------------------------------------------------- # target <- predictmiTargets(gene = brain_mirna[1:100,], # org_assembly = 'hg19', type = "mirna") ## ---- coexp, eval=FALSE------------------------------------------------------- # corAnalysis<-calculateCorr(exp1 = mirna, exp2 = mrna ) ## ---- go annot, eval=FALSE---------------------------------------------------- # geneList <- c("FOXP2","SOX4","HOXC6") # # annot <- annGO(genes = geneList, GOtype = 'BP',org_assembly = 'hg19')[[2]] # ## ----closeby genes, eval=FALSE----------------------------------------------- # neighbour <- getUCSC(bedfile = regionNC, # upstream = 1000, # downstream = 1000, # org_assembly = 'hg19') #