## ----style, echo=FALSE, results="asis", message=FALSE, warnings = FALSE------- knitr::opts_chunk$set(tidy = FALSE, warning = FALSE, message = FALSE,fig.width=6, fig.height=6 ) ## ----set size----------------------------------------------------------------- knitr::opts_knit$set(width = 80) ## ----Install, eval=FALSE, echo=TRUE, include=TRUE----------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("NoRCE") ## ----Load, eval=TRUE, echo=TRUE, include=TRUE--------------------------------- library(NoRCE) ## ----Load package, eval=TRUE, echo=TRUE, include=TRUE------------------------- library(NoRCE) ## ---- Enrichment analysis based on gene neighbourhood when input set is consist of genes, eval=TRUE, echo = FALSE---- ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', near=TRUE, genetype = 'Ensembl_gene') ## ---- Intersection of the nearest genes of the input gene set and the potential target set is carries out for enrichment analysis, eval=TRUE---- mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, target=TRUE) ## ---- Retrieve list of cell-line , eval=TRUE---------------------------------- a<-listTAD(TADName = tad_hg19) ## ---- Enrichment based on TAD cellline, eval=TRUE----------------------------- mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, isTADSearch = TRUE, TAD = tad_hg19) ## ---- Pathway enrichment based on the gen sets that falls into the TAD regions, eval=TRUE---- ncRNAPathway<-genePathwayEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', isTADSearch = TRUE,TAD = tad_hg19, genetype = 'Ensembl_gene') ## ----Custom correlation analysis, eval=TRUE, echo=TRUE------------------------ dataCor <- calculateCorr(exp1 = mirna[,1:50], exp2 = mrna[,1:50])