Supported Assemblies and Organisms

NoRCE supports many organisms and assemblies. To work with supported organisms, the user has to provide the organism assembly, org_assembly, with a keyword that is listed in the table.

Enrichment Analysis Based on Gene Neighbourhood

When the near parameter is set to TRUE, the closest genes for the input gene list are retrieved. The gene neighborhood taken into consideration is controlled by the upstream and downstream parameters. By default, those parameters are set to 10 kb and all genes that fall into 10 kb upstream and downstream of the input genes are retrieved. Also, using searchRegion parameter, the analysis can be performed for only those genes whose exon or intron regions fall into the specified upstream and downstream range of the input genes.

To perform enrichment analysis based on considering only gene neighborhood:

#Set the neighbourhood region as exon
ncGO<-geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly='hg19', near=TRUE, genetype = 'Ensembl_gene')

To perform enrichment analysis based on exon neighbourhood:

#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')

NoRCE accepts the BED formatted input gene regions. To perform the enrichment analysis on gene regions:

#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)

Enrichment Analysis Based on Target Prediction

For a set of miRNA genes, the user can filter the gene set using miRNA target predictions. This analysis is controlled by the target parameter. Once the target parameter is set to TRUE, TargetScan predictions are used to curate the gene list that will be enriched.

To performed GO enrichment based on neighboring coding genes of brain miRNA targeted by the same brain miRNA gene set.

mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, target=TRUE)

Enrichment Analysis Based on Topological Associating Domain Analysis

Gene annotation based on topologically associating domain regions is conducted whether ncRNAs fall into the TAD boundaries and coding gene assignment only those that are in the same TAD boundaries are included in the neighborhood coding gene set. If cell-line(s) for TAD boundaries are specified, only regions that are associated with the given cell-line(s) are considered. User-defined and pre-defined TAD boundaries can be used to find potential gene setlist for enrichment. For human, mouse, and fruit fly, pre-defined TAD boundaries are supplied and custom TAD boundaries must be in a BED format. Cell-lines are controlled by the cellline parameter. Cell-lines can be listed with the listTAD function.

To perform enrichment based on TAD boundaries that also fall into the noncoding gene’s neighborhood

mirGO<-mirnaGOEnricher(gene = brain_mirna, org_assembly='hg19', near=TRUE, isTADSearch = TRUE, TAD = tad_hg19)

User-defined TAD regions can be used as an input for the TAD regions and gene enrichment can be performed based on these custom TAD regions. TAD parameter is provided to input the bed formatted TAD regions.

# 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)

To retrieve the list of cell-lines from the given TAD boundaries:

a<-listTAD(TADName = tad_hg19)

Enrichment Analysis Based on Correlation Analysis

Enrichment based on correlation analysis is conducted with the binary express parameter. For a given cancer, a pre-calculated Pearson correlation coefficient between miRNA-mRNA and miRNA-lncRNA expressions can be used to augment or filter the results. Users can define the correlation coefficient cutoff and cancer of interest with minAbsCor and cancer parameter, respectively. miRCancer.db, the pre-computed miRNA-mRNA and miRNA-lncRNA correlation database, must be downloaded to the local and the path of the database must be given as an input to a databaseFile parameter.

ncGO_tad <- geneGOEnricher(gene = brain_disorder_ncRNA, org_assembly = 'hg19', genetype = 'Ensembl_gene',near = TRUE, express = TRUE, databaseFile = '\\miRCancer\\miRCancer.db', cancer = 'GBMLGG')

Two custom-defined expression data can be utilized to augment or filter the coding genes that are found using the previous analysis. To make this option on, isCustomExp parameter has to set TRUE, and custom expressions should be inputted to the exp1 and exp2 parameters. Expression data must be patient by gene data and headers should be gene names. If no header is defined, label1 and label2 must be used to define the headers. The correlation cutoff can be defined with the minAbsCor parameter.

To perform GO enrichment for the intersection of the miRNA targets and custom RNAseq expression of miRNA and mRNA data in NoRCE repository:

# 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)

To augment the results, isUnionCorGene parameter has to set to TRUE.

# 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)

Enrichment on KEGG, Reactome, WikiPathways

As in GO enrichment analysis, pathway enrichment analysis can be performed based on gene neighborhood, predicted targets, correlation coefficient, and/or topological domain analysis. Each parameter is controlled by the related parameters and HUGO, ENSEMBL gene, ENSEMBL transcript, ENTREZ ID, and miRBase name is supported for the input gene list. Non-coding genes can be annotated and enriched with KEGG, Reactome, and Wiki pathways. genePathwayEnricher and geneRegionPathwayEnricher functions fulfill the pathway enrichment for the genes and regions expect the miRNA genes and for the miRNA mirnaPathwayEnricher and mirnaRegionPathwayEnricher is used. By default, KEGG enrichment is performed.

# KEGG enrichment is performed 
miPathway <- mirnaPathwayEnricher(gene = brain_mirna, org_assembly = 'hg19', near = TRUE, target = TRUE)

To perform Reactome and Wiki enrichment

# 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')

Enrichment on Custom GMT File

Moreover, pathway enriched can be performed based on a custom GMT file. GMT file supports both gene format of ENTREZ ID, Symbol and it is controlled by the isSymbol parameter.

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)

Tabular Format

Users can write the results in a tabular format in a TXT file. To save the result in a tabular format, writeEnrichment function should be called. Users can write sorted all or their defined number of enrichment results using the n parameter. If the n parameter is not defined, all results will be written. Results are sorted based on p-value or p-adjusted value. Users should use the type parameter to identify their sort preferences. By default, results are sorted based on the p-adjusted value. By default, it is tab-separated but the users can use their preferred separator.

The tabular format contains those fields: GO/pathway ID, pathway/GO term, p-value, p-adjust, gene ratio, background gene ratio, number of enriched genes, annotated gene list with the enriched terms, input noncoding gene list that is annotated with enriched protein-coding genes.

writeEnrichment(mrnaObject = ncGO,fileName = "result.txt",sept = "\t",type = "pvalue")

writeEnrichment(mrnaObject = ncGO,fileName = "result.txt",sept = "\t",type = "pvalue", n = 5)

Dot Plot

Results can be visualized in the dot plot. Top enrichment results according to p-value or p-adjust value are provided, also, the number of annotated genes with the enriched term is supplied. Users have an option to select p-value or p-adjust value for visualization with the type parameter also the n parameter should be used for top enrichment results. Dot plots can be used for pathway and GO enrichment.

drawDotPlot(mrnaObject = ncGO, type = "pvalue", n = 25)

Network

NoRCE generates gene - enriched terms network to present the relationship between terms and enriched results. Same with dot plots, type parameter and n parameter can be utilized. By default, nodes are the enriched terms and annotated genes, edges represent the relationship. However, the users can choose to generate a network where nodes input noncoding gene lists that are annotated with enriched protein-coding genes instead of protein-coding genes. This is control by the boolean isNonCode parameter. If it is set to TRUE, input noncoding genes are utilized. Also, by default, GO-term/pathway-term are used for network generation, but, GO IDs or pathway IDs can be used. This is control by the boolean takeID parameter. If it is set to TRUE, the naming decision of the GO/pathway node is GO ID-pathway ID. This network can be used for the pathway and GO enrichment findings.

createNetwork(mrnaObject = ncGO, type = 'pvalue', n = 2, isNonCode = TRUE)

GO DAG

NoRCE draws the GO directed acyclic graph for a given number of enrichments. Enriched GO terms are colored in terms of their p-value or p-adjust value. Users can download PNG or SVG formatted DAGs.

getGoDag(mrnaObject = ncGO,type = 'pvalue', n = 2,filename = 'dag.png', imageFormat = "png")

Reactome and KEGG Map

Reactome and KEGG pathway maps can be generated in a browser. Users should select the enriched pathway, pathway. Even though the Reactome map is visualized in the browser, the user can select PNG or SVG as an image format with imageFormat parameter.

getKeggDiagram(mrnaObject = ncRNAPathway, org_assembly ='hg19', pathway = ncRNAPathway@ID[1])

getReactomeDiagram(mrnaObject = ncRNAPathway, pathway = miGO1@ID[3], imageFormat = "png")

Getting miRNA Targets

You can get the miRNA targets for a given input. The output is miRNA-mRNA target pairs.

target <- predictmiTargets(gene = brain_mirna[1:100,],                   
                     org_assembly = 'hg19', type = "mirna")

Custom Co-expression Analysis

NoRCE can also perform co-expression analysis between two datasets. exp1 and exp2 parameters take expression data and each column must contain gene expression. If the expression data does not contain the header information, label1 and label2 parameter must be used for this information. calculateCorr function calculates the variance of each gene’s expression, and genes that vary lesser than the user-defined variance cutoff, varCutoff, are excluded from the analysis. The users can select their preferred correlation method, corrMethod, with an alternative hypothesis, alternate for a given p-value, confidence and minimum correlation cut off, pcut, conf,corCutoff, respectively.

corAnalysis<-calculateCorr(exp1 = mirna, exp2 = mrna )

Identifying GO Annotations For the Given Genes

NoRCE can identify the GO annotatıon for a given gene set. To only annotate the genes with GO terms, annGO function should be called. The input gene list should be in the HUGO gene format.

geneList <- c("FOXP2","SOX4","HOXC6")

annot <- annGO(genes = geneList, GOtype = 'BP',org_assembly = 'hg19')[[2]]

Get Closeby Genes

User can detect closeby genes in a given upstream and downstream ranges. annGO

neighbour <- getUCSC(bedfile = regionNC,
                    upstream = 1000,
                    downstream = 1000,
                    org_assembly = 'hg19')