associate_phenotype: Modules phenotpic association

In Kumquatum/GWENA: Pipeline for augmented co-expression analysis

Modules phenotpic association

Description

Compute the correlation between all modules and the phenotypic variables

Usage

associate_phenotype(
  eigengenes,
  phenotypes,
  cor_func = c("pearson", "spearman", "kendall", "other"),
  your_func = NULL,
  id_col = NULL,
  ...
)

Arguments

eigengenes	matrix or data.frame, eigengenes of the modules. Provided by the output of modules_detection.
phenotypes	matrix or data.frame, phenotypes for each sample to associate.
cor_func	string, name of the correlation function to be used. Must be one of "pearson", "spearman", "kendall", "other". If "other", your_func must be provided
your_func	function returning a correlation matrix. Final values must be in [-1;1] range
id_col	string or vector of string, optional name of the columns containing the common id between eigengenes and phenotypes.
...	any arguments compatible with cor.

Value

A list of two data.frames : associations modules/phenotype and p.values associated to this associations

Examples

eigengene_mat <- data.frame(mod1 = rnorm(20, 0.1, 0.2),
mod2 = rnorm(20, 0.2, 0.2))
phenotype_mat <- data.frame(phenA = sample(c("X", "Y", "Z"), 20,
                            replace = TRUE),
                            phenB = sample(c("U", "V"), 20, replace = TRUE),
                            stringsAsFactors = FALSE)
association <- associate_phenotype(eigengene_mat, phenotype_mat)

Kumquatum/GWENA documentation built on July 7, 2023, 3:41 p.m.