regenrich_rankScore {RegEnrich} | R Documentation |
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
|
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))