confounder: Confounder analysis

In HuaZou/MicrobiomeAnalysis: Data analysis toolkits in metagenomics

Confounder analysis

Description

Confounding variables may mask the actual differential features. This function utilizes constrained correspondence analysis (CCA) to measure the confounding factors.

Usage

confounder(
  ps,
  target_var,
  norm = "none",
  confounders = NULL,
  permutations = 999,
  ...
)

Arguments

ps	a phyloseq::phyloseq object.
target_var	character, the variable of interest
norm	norm the methods used to normalize the microbial abundance data. See normalize() for more details.
confounders	the confounding variables to be measured, if NULL, all variables in the meta data will be analyzed.
permutations	the number of permutations, see vegan::anova.cca().
...	extra arguments passed to vegan::anova.cca().

Value

a data.frame contains three variables: confounder, pseudo-F and p value.

Examples

data(caporaso)
confounder(caporaso, "SampleType", confounders = "ReportedAntibioticUsage")

HuaZou/MicrobiomeAnalysis documentation built on May 13, 2024, 11:10 a.m.