results_expr {RegEnrich} | R Documentation |
results_expr accesses raw expression data.
results_DEA accesses results from differential expression analysis.
results_topNet accesses results from network inference.
retults_enrich accesses results from FET/GSEA enrichment analysis.
results_score accesses results from regulator scoring and ranking.
results_expr(object) results_DEA(object) results_topNet(object) results_enrich(object) results_score(object)
object |
RegenrichSet object. |
results_expr retures an expression matrix.
results_DEA returns a list result of differentila analysis.
results_topNet returns a TopNetwork object.
results_enrich returns an Enrich object by either FET or GSEA method.
results_score returns an data frame of summarized ranking scores of regulators.
# 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) results_expr(object) results_DEA(object) # Network inference using 'COEN' method object = regenrich_network(object) results_topNet(object) # Enrichment analysis by Fisher's exact test (FET) object = regenrich_enrich(object) results_enrich(object) # Regulators ranking object = regenrich_rankScore(object) results_score(object)