regenrich_rankScore: Regulator scoring and ranking
In WTaoUMC/RegEnrich: Gene regulator enrichment analysis

Description Usage Arguments Value See Also Examples

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

regenrich_rankScore(object)

## S4 method for signature 'RegenrichSet'
regenrich_rankScore(object)

object

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

This function returns a 'RegenrichSet' object with an updated 'resScore' slots, which is a 'regEnrichScore' (also 'data.frame') object, and an updated 'paramsIn' slot. In the 'regEnrichScore' object there are five columns, which are 'reg' (regulator), 'negLogPDEA' (-log10(p values of differential expression analysis)), 'negLogPEnrich' (-log10(p values of enrichment analysis), 'logFC' (log2 fold changes), and 'score' (RegEnrich ranking score).

Previous step regenrich_enrich.

# 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)

# Regulators ranking
(object = regenrich_rankScore(object))