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Compute Weighted TF-coactivity Network Analysis (WTCNA)

compute.WTCNA.Rd

Construct a weighted signed or unsigned network using TF activity to cluster protein regulators into modules that share similar activity patterns. Each TF module will have a sample-level score represented by the eigenvalue of the module.

Usage

# S3 method for class 'WTCNA'
compute(
  TFs.matrix,
  batch = FALSE,
  network.type = "signed",
  clustering.method = "ward.D2",
  minMod = 15,
  corr_mod = 0.9,
  cor_type = "p",
  verbose = F,
  file.name = NULL,
  softPower = NULL,
  return = T
)

Arguments

TFs.matrix

Matrix of TF activity (samples x TFs).

batch

Logical; if TRUE, performs consensus WGCNA across cohorts provided as a list.

network.type

Network type: "signed", "unsigned", "signed hybrid", or "distance". Default is "signed".

clustering.method

Clustering method for hierarchical clustering. Default is "ward.D2".

minMod

Minimum number of TFs per module. Default is 15.

corr_mod

Correlation threshold (0-1) for merging similar modules. Default is 0.9.

cor_type

Correlation type for adjacency calculation: "p" (Pearson), "s" (Spearman). Default is "p".

verbose

Boolen value to whether print or no the function messages

file.name

Optional character suffix used when writing WTCNA outputs.

softPower

Optional numeric value specifying the soft-thresholding power to be used when constructing

return

Logical, whether to save output plots and module list to "Results/". Default is TRUE.

Value

A named list with:

  • TFs module matrix: Matrix of module eigengenes (samples x modules).

  • TFs colors: Vector of module colors assigned to each TF.

  • TFs per module: List of TF names in each module.

  • Proportion of variance: Matrix of variance explained per module.

References

Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559. https://doi.org/10.1186/1471-2105-9-559

Examples


data("tfs.tuto")
network <- compute.WTCNA(tfs.tuto, corr_mod = 0.9, clustering.method = "ward.D2", return = FALSE)
#> Warning: executing %dopar% sequentially: no parallel backend registered