regenrich_enrich {RegEnrich} | R Documentation |
As the thrid step of RegEnrich analysis, enrichment analysis is followed by differential expression analysis (regenrich_diffExpr), and regulator-target network inference (regenrich_network).
regenrich_enrich(object, ...) ## S4 method for signature 'RegenrichSet' regenrich_enrich(object, ...)
object |
a 'RegenrichSet' object, to which
|
... |
arguments for enrichment analysis.
After constructing a 'RegenrichSet' object using |
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.
Previous step regenrich_network
,
and next step regenrich_rankScore
.
# 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"))