sva_analysis: Perform SVA confounder analysis

In Keyeoh/svconfound: A simple SVD/SVA confounding analysis framework

Description

Perform SVA confounder analysis and association tests between the Surrogate Variables of the given input values and the phenotype data.

Usage

sva_analysis(values, pdata, main_formula, null_formula = NULL,
  vfilter = NULL, rgset = NULL)

Arguments

values	A matrix containing the data to analyze. Rows represent variables and columns samples.
pdata	A data.frame containing the phenotype information for the samples.
main_formula	A formula describing the main model of interest.
null_formula	A formula describing the null model of interest.
vfilter	The number of most variable rows to use in order to estimate the number of surrogate variables.
rgset	If not NULL, compute association with control probes data contained in the provided RGChannelSet.

Details

This function performs a series of association tests between the Surrogate Variables of the input values and the provided phenotype data.

If a RGChannelSet is given, it can also compute the association with the control probes. This implementation uses Linear models.

Value

A list containing the number of Surrogate Variables (num_sv), the results of the confounding analysis tests (significance), and the Surrogate Variables obtained by SVA (surrogates).

Keyeoh/svconfound documentation built on May 15, 2019, 1:25 p.m.