regenrich_network: Regulator-target network inference step

In RegEnrich: Gene regulator enrichment analysis

Description

As the second step of RegEnrich analysis, network inference is followed by differential expression analysis (regenrich_diffExpr).

Provide a network to 'RegenrichSet' object.

Usage

regenrich_network(object, ...)

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

regenrich_network(object) <- value

## S4 replacement method for signature 'RegenrichSet,TopNetwork'
regenrich_network(object) <- value

## S4 replacement method for signature 'RegenrichSet,data.frame'
regenrich_network(object) <- value

Arguments

object	a 'RegenrichSet' object, to which regenrich_diffExpr function has been already applied.
...	arguments for network inference. After constructing a 'RegenrichSet' object using RegenrichSet function, all arguments for RegEnrich analysis have been initialized and stored in 'paramsIn“ slot. The arguments for network inference can be re-specified here. These arguments include 'networkConstruction', 'reg', 'rowSample', 'softPower', 'networkType', 'TOMDenom', 'RsquaredCut', 'edgeThreshold', 'K', 'nbTrees', 'importanceMeasure', 'trace', 'BPPARAM', 'minR', 'topNetPercent', and 'directed'. See RegenrichSet function for more details about these arguments.
value	either a 'TopNetwork' object or 'data.frame' object. If value is a 'data.frame' object, then the number of columns of

Value

This function returns a 'RegenrichSet' object with an updated 'network' and 'topNetP' slots, which are 'TopNetwork' objects, and an updated 'paramsIn' slot. See TopNetwork-class class for more details.

This function returns a 'RegenrichSet' object with an updated 'network' and 'topNetP' slots, which are 'TopNetwork' objects, and an updated 'paramsIn' slot. See TopNetwork-class class for more details.

See Also

Previous step regenrich_diffExpr, and next step regenrich_enrich. User defined network regenrich_network<-

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))

RegEnrich documentation built on March 7, 2021, 2 a.m.