DA.poi: Poisson glm

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

Poisson glm

Description

Apply poisson generalized linear models on multiple features with one predictor With log(librarySize) as offset. Mixed-effect model is used when a paired argument is included, with the paired variable as a random intercept.

Usage

DA.poi(
  data,
  predictor,
  paired = NULL,
  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
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
out.all	If TRUE will output results and p-values from anova (drop1 if paired != NULL). 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 glm/glmer function
...	Additional arguments for the glm/glmer functions

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 Poisson regression on each feature
res <- DA.poi(data = mat, predictor = pred)

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