consensus_modules: Identify consensus modules across independent data sets

In almeidasilvaf/BioNERO: Biological Network Reconstruction Omnibus

Identify consensus modules across independent data sets

Description

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")

almeidasilvaf/BioNERO documentation built on Oct. 9, 2024, 1:49 a.m.