runDiffTest: Performs differential abundance testing

In zoecastillo/microbiomeExplorer: Microbiome Exploration App

Performs differential abundance testing

Description

This function performs differential abundance testing between groups of a specified phenotype. Four methods are available: limma, Kruskal-Wallis, ZILN and DESeq2 (see details).

Usage

runDiffTest(
  aggdat,
  level,
  phenotype,
  phenolevels = NULL,
  log = TRUE,
  coef = NULL,
  method = c("limma", "Kruskal-Wallis", "DESeq2")
)

Arguments

aggdat	aggregated MRExperiment
level	Feature level.
phenotype	Phenotype to test.
phenolevels	levels of the phenotype to restrict the comparison to
log	Log2 transform data. Default is TRUE.
coef	Numeric which indicates which pairwise comparison to analyze when there are more than two groups. Corresponds to the column number of the model matrix produced by designPairs(). If NULL, a test of any difference between all groups is performed.
method	Differential testing method. One of "limma" (default), "Kruskal-Wallis", or "DESeq2".

Details

limma is a differential expression tool for microarray data using linear models. It can also be applied to microbiome data.

The Kruskal-Wallis test is a non-parametric rank test which examines if groups come from the same distribution. A significant result indicates at least one group is distributionally different than another group.

ZILN is a zero-inflated log-normal model implemented in fitFeatureModel() of the metagenomeSeq package.

DeSeq2 performs differential gene expression analysis based on the negative binomial distribution

Value

data.frame holding results of the differential analysis

Examples

data("mouseData", package = "metagenomeSeq")
aggdat <- aggFeatures(mouseData, level = "genus")
runDiffTest(aggdat = aggdat,level = "genus", 
            phenotype = "diet", method = "Kruskal-Wallis")

zoecastillo/microbiomeExplorer documentation built on Sept. 11, 2022, 7:40 a.m.