associate_phenotype {GWENA} | R Documentation

## 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)

[Package GWENA version 1.2.0 Index]