Identify consensus modules across independent data sets

Usage

consensus_modules(
  exp_list,
  metadata,
  power,
  cor_method = "spearman",
  net_type = "signed hybrid",
  module_merging_threshold = 0.8,
  TOM_type = NULL,
  verbose = FALSE
)

Arguments

exp_list: A list containing the expression data frames with genes in row names and samples in column names or `SummarizedExperiment` objects. The list can be created by using list(exp1, exp2, ..., expn).
metadata: A data frame containing sample names in row names and sample annotation in the first column. Ignored if `exp_list` is a list of `SummarizedExperiment` objects, since the function will extract colData.
power: Numeric vector of beta power for each expression set as calculated by consensus_SFT_fit.
cor_method: Correlation method used for network reconstruction. One of "spearman" (default), "biweight", or "pearson".
net_type: Network type. One of "signed hybrid" (default), "signed" or "unsigned".
module_merging_threshold: Correlation threshold to merge similar modules into a single one. Default: 0.8.
TOM_type: Character indicating the type of Topological Overlap Matrix to (TOM) create. One of 'unsigned', 'signed', 'signed Nowick', 'unsigned 2', 'signed 2', and 'signed Nowick 2'. By default, TOM type is automatically selected based on network type.
verbose: Logical indicating whether to display progress messages or not. Default: FALSE.

Value

A list containing 4 elements:

consMEs: Consensus module eigengenes
exprSize: Description of the multi-set object returned by the function WGCNA::checkSets
sampleInfo: Metadata for each expression set
genes_cmodules: Data frame of genes and consensus modules
dendro_plot_objects: Objects to be used in dendrogram plotting

Examples

set.seed(12)
data(zma.se)
filt.zma <- filter_by_variance(zma.se, n=500)
zma.set1 <- filt.zma[, sample(colnames(filt.zma), size=20, replace=FALSE)]
zma.set2 <- filt.zma[, sample(colnames(filt.zma), size=20, replace=FALSE)]
list.sets <- list(zma.set1, zma.set2)
# SFT power previously identified with consensus_SFT_fit()
cons_mod <- consensus_modules(list.sets, power = c(11, 13),
                              cor_method = "pearson")
#> ..connectivity..
#> ..matrix multiplication (system BLAS)..
#> ..normalization..
#> ..done.
#> ..connectivity..
#> ..matrix multiplication (system BLAS)..
#> ..normalization..
#> ..done.
#>  ..done.
#>  multiSetMEs: Calculating module MEs.
#>    Working on set 1 ...
#>    Working on set 2 ...