Apply dimensionality reduction using Weighted Gene Correlation Network Analysis

Performs Weighted gene correlation network analysis (WGCNA) and packages both the input data and subsequent results into a ModularExperiment. Calls runWGCNA to perform the analysis; see its documentation page for more information on the ICA method, parameters and outputs.

Usage

identifyModules(
  X,
  power,
  center_X = TRUE,
  scale_X = TRUE,
  assay_name = "normal",
  ...
)

Arguments

X: Either a SummarizedExperiment object or a matrix containing data to be subject to WGCNA. X should have rows as features and columns as samples.
power: An integer representing the soft-thresholding power to be used to define modules. See the assessSoftThreshold function for aid in determining this parameter.
center_X: If TRUE, X is centered (i.e., features / rows are transformed to have a mean of 0) prior to WGCNA.
scale_X: If TRUE, X is scaled (i.e., features / rows are transformed to have a standard deviation of 1) before WGCNA.
assay_name: If X is a SummarizedExperiment, then this should be the name of the assay to be subject to WGCNA.
...: Additional arguments to be passed to runWGCNA.

Value

A ModularExperiment is returned containing the input data (i.e., the original data matrix in addition to other slots if a SummarizedExperiment was used as input). Additionally contains the results of module analysis, stored in the reduced and assignments slots. The center_X, scale_X, loadings, threshold and dendrogram slots may also be filled depending on the arguments given to identifyModules.

Author

Jack Gisby

Examples

# Get the airway data as a SummarizedExperiment (with a subset of features)
set.seed(2)
airway_se <- ReducedExperiment:::.getAirwayData(n_features = 500)

# Select soft-thresholding power to use (use capture.output to hide WGCNA's prints)
WGCNA::disableWGCNAThreads()
invisible(capture.output(fit_indices <- assessSoftThreshold(airway_se)))
estimated_power <- fit_indices$Power[fit_indices$estimated_power]

# Identify modules using WGCNA
airway_me <- identifyModules(airway_se, verbose = 0, power = estimated_power)
airway_me
#> class: ModularExperiment 
#> dim: 500 8 6 
#> metadata(1): ''
#> assays(3): counts normal transformed
#> rownames(500): ENSG00000186523 ENSG00000203871 ... ENSG00000147439
#>   ENSG00000116661
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(9): SampleName cell ... Sample BioSample
#> 6 components