## ----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 <- 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 <- 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')
#  

