regenrich_network {RegEnrich} | R Documentation

## Regulator-target network inference step

### 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))

[Package RegEnrich version 1.0.1 Index]