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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

compute.WTCNA(
  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 (WGCNA::blockwiseConsensusModules()) across cohorts provided as a list of matrices. In this mode clustering.method and corr_mod are not used (modules are merged with a fixed mergeCutHeight = 0.25), and module eigengenes are scaled within each cohort.

network.type

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

clustering.method

Clustering method for hierarchical clustering (single-cohort mode only). Default is "ward.D2".

minMod

Minimum number of TFs per module. Default is 15.

corr_mod

Correlation threshold (0-1) above which module eigengenes are merged (single-cohort mode only). Default is 0.9.

cor_type

Correlation used to pick the soft-threshold power and build the adjacency matrix: "p" (Pearson) or "s" (Spearman). Spearman is only available when batch = FALSE, because WGCNA consensus modules only support Pearson correlation. 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 used to build the adjacency matrix (one value per cohort when batch = TRUE). If NULL, the power whose scale-free fit \(R^2\) is closest to 0.9 is chosen automatically.

return

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

Value

A named list with:

  • TFs module matrix: Scaled module eigengenes (samples x modules). In batch mode, samples are concatenated in cohort order.

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

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

  • Proportion of variance: Variance explained per module (single-cohort mode only).

  • TFs_matrix: The TF activity matrix used (a list of per-cohort matrices restricted to shared TFs in batch mode).

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