regenrich_enrich: Enrichment analysis step

In WTaoUMC/RegEnrich: Gene regulator enrichment analysis

Description

As the thrid step of RegEnrich analysis, enrichment analysis is followed by differential expression analysis (regenrich_diffExpr), and regulator-target network inference (regenrich_network).

Usage

regenrich_enrich(object, ...)

## S4 method for signature 'RegenrichSet'
regenrich_enrich(object, ...)

Arguments

object

a 'RegenrichSet' object, to which regenrich_diffExpr, and regenrich_network, functions have been already applied.

...

arguments for enrichment analysis. After constructing a 'RegenrichSet' object using RegenrichSet function, all arguments for RegEnrich analysis have been initialized and stored in 'paramsIn“ slot. The arguments for enrichment analysis can be re-specified here. These arguments include 'enrichTest', 'namedScoresCutoffs', 'minSize', 'maxSize', 'pvalueCutoff','qvalueCutoff', 'regAltName', 'universe', 'minSize', 'maxSize', 'pvalueCutoff', and 'nperm'. See RegenrichSet function for more details about these arguments.

Value

This function returns a 'RegenrichSet' object with an updated 'resEnrich' slots, which is 'Enrich' objects, and an updated 'paramsIn' slot. See Enrich-class function for more details about 'Enrich' class.

See Also

Previous step regenrich_network, and next step regenrich_rankScore.

Examples

# library(RegEnrich)
data("Lyme_GSE63085")
data("TFs")

data = log2(Lyme_GSE63085$FPKM + 1)
colData = Lyme_GSE63085$sampleInfo

# Take first 2000 rows for example
data1 = data[seq(2000), ]

design = model.matrix(~0 + patientID + week, data = colData)

# Initializing a 'RegenrichSet' object
object = RegenrichSet(expr = data1,
                      colData = colData,
                      method = 'limma', minMeanExpr = 0,
                      design = design,
                      contrast = c(rep(0, ncol(design) - 1), 1),
                      networkConstruction = 'COEN',
                      enrichTest = 'FET')


# Differential expression analysis
object = regenrich_diffExpr(object)

# Network inference using 'COEN' method
object = regenrich_network(object)

# Enrichment analysis by Fisher's exact test (FET)
(object = regenrich_enrich(object))

# Enrichment analysis by Fisher's exact test (GSEA)
(object = regenrich_enrich(object, enrichTest = "GSEA"))

WTaoUMC/RegEnrich documentation built on Aug. 4, 2021, 4:11 p.m.