DA.mva: Mvabund

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

Mvabund

Description

Implementation of mvabund manyglm for DAtest. With negative binomial family and an offset of log(LibrarySize) when relative = TRUE

Usage

DA.mva(
  data,
  predictor,
  paired = NULL,
  covars = NULL,
  relative = TRUE,
  p.adj = "fdr",
  coeff = 2,
  coeff.ref = 1,
  resamp = "montecarlo",
  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)
relative	Logical. Whether log(librarySize) should be used as offset. Default TRUE
p.adj	Character. P-value adjustment. Default "fdr". See p.adjust for details. Alternatively, "mva" uses mvabunds adjusted p-values
coeff	Integer. The p-value and log2FoldChange will be associated with this coefficient. Default 2, i.e. the 2. level of the predictor.
coeff.ref	Integer. Reference level of the predictor. Will only affect the log2FC and ordering columns on the output. Default the intercept, = 1
resamp	Resample method for estimating p-values. Passed to summary.manyglm. Default "montecarlo"
allResults	If TRUE will return raw results from the mvabund function
...	Additional arguments for the manyglm and summary.manyglm functions

Value

A data.frame with with results.

Examples

# Creating random count_table and predictor
set.seed(4)
mat <- matrix(rnbinom(100, size = 0.1, mu = 500), nrow = 10, ncol = 10)
rownames(mat) <- 1:10
pred <- c(rep("Control", 5), rep("Treatment", 5))

# Running mvabund manyglm
res <- DA.mva(data = mat, predictor = pred)

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