runDiffTest {microbiomeExplorer} | R Documentation

## 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", "ZILN", "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", "ZILN", 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")

[Package microbiomeExplorer version 1.4.0 Index]