Compute TF Network Classification

Classifies transcription factor (TF) modules into clusters based on their correlations with pathway activity values across samples. Uses hierarchical clustering and silhouette width to determine the optimal number of clusters.

Usage

# S3 method for class 'TF.network.classification'
compute(tf.network, pathways.features, return = T)

Arguments

tf.network

A list containing TF module eigengenes, typically output from compute.WTCNA()

pathways.features

A matrix with pathway activities, typically from compute.pathway.activity()

return

Logical. If TRUE, intermediate plots (e.g. silhouette, dendrogram, PCA) are saved in the Results/ directory. Default is TRUE.

Value

A named list of TF module clusters.

Examples

if (FALSE) { # \dontrun{
data("network.tuto")
data("counts.norm.tuto")
pathways <- compute.pathway.activity(counts.norm.tuto)
tfs.modules.clusters <- compute.TF.network.classification(tf.network = network.tuto,
                                                          pathways.features = pathways,
                                                          return = FALSE)
} # }