DA.znb: Zero inflated Negative Binomial glm

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

Zero inflated Negative Binomial glm

Description

Apply zero-inflated negative binomial generalized linear model to multiple features, with one independent variable With log(librarySize) as offset.

Usage

DA.znb(
  data,
  predictor,
  covars = NULL,
  relative = TRUE,
  out.all = NULL,
  p.adj = "fdr",
  coeff = 2,
  coeff.ref = 1,
  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
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
out.all	If TRUE will output results and p-values from drop1. If false will output results for 2. level of the predictor. 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 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
allResults	If TRUE will return raw results from the zeroinfl function
...	Additional arguments for the zeroinfl 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 Zero-inflated Negative Binomial regression on each feature
res <- DA.znb(data = mat, predictor = pred)

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