cinaR (version 0.2.3)

run_enrichment: run_enrichment

Description

This function is run, if the enrichment pipeline wants to be called afterwards. Setting reference genome to the same genome which cinaR was run should be given to this function!

Usage

run_enrichment(
  results,
  geneset = NULL,
  experiment.type = "ATAC-Seq",
  reference.genome = NULL,
  enrichment.method = NULL,
  enrichment.FDR.cutoff = 1,
  background.genes.size = 20000,
  verbose = TRUE
)

Arguments

results

list, DA peaks list for different contrasts

geneset

Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using `read.gmt` function from `qusage` package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.

experiment.type

The type of experiment either set to "ATAC-Seq" or "RNA-Seq"

reference.genome

genome of interested species. It should be 'hg38', 'hg19' or 'mm10'.

enrichment.method

There are two methodologies for enrichment analyses, Hyper-geometric p-value (HPEA) or Geneset Enrichment Analyses (GSEA).

enrichment.FDR.cutoff

FDR cut-off for enriched terms, p-values are corrected by Benjamini-Hochberg procedure

background.genes.size

number of background genes for hyper-geometric p-value calculations. Default is 20,000.

verbose

prints messages through running the pipeline

Value

list, enrichment analyses results along with corresponding differential analyses outcomes

Examples

# NOT RUN {
library(cinaR)
data(atac_seq_consensus_bm) # calls 'bed'

# a vector for comparing the examples
contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE),
                    function(x){x[1]})[4:25]

results <- cinaR(bed, contrasts, reference.genome = "mm10", run.enrichment = FALSE)

results_with_enrichment <- run_enrichment(results, reference.genome = "mm10")
# }