plotSoftPower {RegEnrich} | R Documentation

## Plot soft power for WGCNA analysis

### Description

Plot soft power and corresponding scale free topology fitting index to find a proper soft power for WGCNA analysis.

### Usage

    plotSoftPower(
      expr,
      rowSample = FALSE,
      weights = NULL,
      powerVector = c(seq(10), seq(12, 20, by = 2)),
      RsquaredCut = 0.85,
      networkType = "unsigned",
      removeFirst = FALSE,
      nBreaks = 10,
      corFnc = WGCNA::cor,
      corOptions = list(use = "p")
    )

### Arguments

`expr` | Gene expression data, either a matrix or a data frame. By default, each row represents a gene, each column represents a sample.
`rowSample` | logic. If `TRUE`, each row represents a sample. The default is `FALSE`.
`weights,` | optional observation weights for `expr` to be used in correlation calculation.
`powerVector` | a vector of soft thresholding powers for which the scale free topology fit indices are to be calculated.
`RsquaredCut` | desired minimum scale free topology fitting index R^2. The default is 0.85.
`networkType` | character, network type. Allowed values are (unique abbreviations of) "unsigned" (default), "signed", "signed hybrid". See `adjacency`.
`removeFirst,` | should the first bin be removed from the connectivity histogram? The default is FALSE.
`nBreaks,` | number of bins in connectivity histograms. The default is 10.
`corFnc,` | correlation function to be used in adjacency calculation. The default is the `cor` function in WGCNA.
`corOptions,` | a named list of options to the correlation function specified in corFnc. The default is list(use = "p").

### Value

a list of three elements: `powerEstimate`, `fitIndices`, and `plot`. `powerEstimate` is an estimate of an appropriate soft-thresholding power. `fitIndices` is a data frame containing the fit indices for scale free topology. The `plot` is a ggplot object.

### Examples

    data(Lyme_GSE63085)
    log2FPKM = log2(Lyme_GSE63085$FPKM + 1)
    log2FPKMhi = log2FPKM[rowMeans(log2FPKM) >= 10^-3, , drop = FALSE]
    log2FPKMhi = head(log2FPKMhi, 3000) # First 3000 genes for example

    softP = plotSoftPower(log2FPKMhi, RsquaredCut = 0.85)
    print(softP)

[Package RegEnrich version 1.2.0 Index]