comEigenGene: Computing eigen-genes or eigen-microRNAs from detected...

In YC3/mirNet: microRNA netowrk analyses

Description

comEigenGene computes eigen genes/microRNAs from each module detected in co-expression network, and then computes a P-value from the correlation of eigem gene/microRNA expression and phenotypes.

Usage

comEigenGene(
  data,
  pheno,
  moduleList,
  type = "eigen",
  test = "categorical",
  pc = 1
)

Arguments

data	A matrix, the normalized gene/microRNA expression dataset, should be a numeric matrix, with rows referring to genes/microRNAs and columns to samples.
pheno	A vector of sample phenotypes. Sample phenotype in a scientific research could be categorical (treatment/control), or continuous (age). If you have multiple phenotypes, use a list with names denoting the corresponding phenotype.
moduleList	A list, entries are vectors of genes/microRNAs in detected modules.
type	A string, denoting the type of summarization of expression of genes/microRNAs, 'egein' for principal component analysis, 'average' for an average of gene/microRNA expression, default to 'eigen'.
test	A string, 'categorical' if sample phenotype if categorical, 'continuous' if sample phenotype is continuous, default to 'categorical'.
pc	Numeric, the number of principal components you want to show in the figure, default to 1.

Value

A list with P-values as well as detected eigen genes/microRNAs from each module.

Examples

comEigenGene(data.norm, pheno.v, modules.l, type = 'eigen', test = 'categorical')

YC3/mirNet documentation built on Sept. 3, 2020, 3:25 a.m.