consensus_modules {BioNERO} | R Documentation |
Identify consensus modules across independent data sets
consensus_modules( exp_list, metadata, power, cor_method = "spearman", net_type = "signed hybrid", module_merging_threshold = 0.8, verbose = FALSE )
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 |
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 |
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. |
verbose |
Logical indicating whether to display progress messages or not. Default: FALSE. |
A list containing 4 elements:
Consensus module assignments
Consensus module eigengenes
Description of the multi-set object returned by the function WGCNA::checkSets
Metadata for each expression set
Data frame of genes and consensus modules
Objects to be used in dendrogram plotting
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")