DA.erq2: EdgeR quasi-likelihood - RLE normalization

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

EdgeR quasi-likelihood - RLE normalization

Description

Implementation of edgeR quasi-likelihood test for DAtest

Usage

DA.erq2(
  data,
  predictor,
  paired = NULL,
  covars = NULL,
  out.all = NULL,
  p.adj = "fdr",
  coeff = 2,
  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. Either a Factor or Numeric, OR if data is a phyloseq object the name of the variable in sample_data(data) in quotation
paired	For paired/blocked experimental designs. Either a Factor with Subject/Block ID for running paired/blocked analysis, 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)
out.all	If TRUE will output one p-value for all levels of the predictor. If FALSE outputs p-value and logFC from one level of the predictor specified by coeff. If NULL (default) set as TRUE for multi-class predictor and FALSE otherwise
p.adj	Character. P-value adjustment. Default "fdr". See p.adjust for details
coeff	Integer. The p-value and logFC will be associated with this coefficient when out.all = FALSE. Default 2, i.e. the 2. level of the predictor.
allResults	If TRUE will return raw results from the glmQLFTest function
...	Additional arguments for the calcNormFactors, estimateDisp, glmQLFit and glmQLFTest 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.erq2(data = mat, predictor = pred)

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