DA.lao2: ANOVA

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

ANOVA

Description

Apply ANOVA to multiple features with one predictor, with log transformation of relative abundances.

Usage

DA.lao2(
  data,
  predictor,
  covars = NULL,
  p.adj = "fdr",
  delta = 0.001,
  allResults = FALSE,
  ...
)

Arguments

data	Either a matrix with counts/abundances, OR a phyloseq object. If a matrix/data.frame is provided rows should be taxa/genes/proteins and columns samples
predictor	The predictor of interest. Factor, OR if data is a phyloseq object the name of the variable in sample_data(data) in quotation
covars	Either a named list with covariables, OR if data is a phyloseq object a character vector with names of the variables in sample_data(data)
p.adj	Character. P-value adjustment. Default "fdr". See p.adjust for details
delta	Numeric. Pseudocount for the log transformation. Default 0.001
allResults	If TRUE will return raw results from the aov function
...	Additional arguments for the aov functions

Value

A data.frame with with results.

Examples

# Creating random count_table and predictor
set.seed(4)
mat <- matrix(rnbinom(1500, size = 0.1, mu = 500), nrow = 100, ncol = 15)
rownames(mat) <- 1:100
pred <- c(rep("A", 5), rep("B", 5), rep("C", 5))

# Running ANOVA on each feature
res <- DA.lao2(data = mat, predictor = pred)

Russel88/DAtest documentation built on March 24, 2022, 3:50 p.m.