Correlate set-specific modules and consensus modules to sample information

Usage

consensus_trait_cor(consensus, cor_method = "pearson", metadata_cols = NULL)

Arguments

consensus: Consensus network returned by consensus_modules.
cor_method: Correlation method to be used. One of 'spearman' or 'pearson'. Default: 'pearson'.
metadata_cols: A vector (either numeric or character) indicating which columns should be extracted from column metadata if exp is a `SummarizedExperiment` object. The vector can contain column indices (numeric) or column names (character). By default, all columns are used.

Value

Data frame of consensus module-trait correlations and p-values, with the following variables:

trait: Factor, trait name. Each trait corresponds to a variable of the sample metadata (if numeric) or levels of a variable (if categorical).
ME: Factor, module eigengene.
cor: Numeric, correlation.
pvalue: Numeric, correlation P-values.
group: Character, name of the metadata variable.

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()
consensus <- 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 ...
consensus_trait <- consensus_trait_cor(consensus, cor_method = "pearson")