Expression Methods

DA.zig

R Documentation

MetagenomeSeq ZIG

Description

Implementation of Metagenome zero-inflated gaussian model for DAtest

Usage

DA.zig(
  data,
  predictor,
  paired = NULL,
  covars = NULL,
  p.adj = "fdr",
  by = 2,
  eff = 0.5,
  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)`
`p.adj`	Character. P-value adjustment. Default "fdr". See `p.adjust` for details
`by`	Column number or column name specifying which coefficient or contrast of the linear model is of interest (only for categorical `predictor`). Default 2
`eff`	Filter features to have at least a `eff` quantile or number of effective samples, passed to `MRtable`
`allResults`	If TRUE will return raw results from the `fitZig` function
`...`	Additional arguments for the `fitZig` function

Value

A data.frame with with results.

Examples

# Creating random count_table and predictor
set.seed(4)
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 MetagenomeSeq Zero-inflated Gaussian
res <- DA.zig(data = mat, predictor = pred)

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