run_enrichment {cinaR} | R Documentation |
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
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")