DA.ere2: EdgeR exact test - RLE normalization

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

EdgeR exact test - RLE normalization

Description

Implementation of edgeR exact test for DAtest

Usage

DA.ere2(data, predictor, p.adj = "fdr", ...)

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
p.adj	Character. P-value adjustment. Default "fdr". See p.adjust for details
...	Additional arguments for the calcNormFactors, estimateCommonDisp, estimateTagwiseDisp and exactTest functions

Value

A data.frame with with results.

Examples

# Creating random count_table and predictor
set.seed(5)
mat <- matrix(rnbinom(1000, size = 0.1, mu = 500), nrow = 100, ncol = 10)
rownames(mat) <- 1:100
pred <- c(rep("Control", 5), rep("Treatment", 5))

# Running edgeR
res <- DA.ere2(data = mat, predictor = pred)

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