DA.lia: LIMMA - Multiplicative zero-correction and additive log-ratio...

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

LIMMA - Multiplicative zero-correction and additive log-ratio normalization.

Description

Note: Last feature in the data is used as reference for the log-ratio transformation.

Usage

DA.lia(
  data,
  predictor,
  paired = NULL,
  covars = NULL,
  out.all = NULL,
  p.adj = "fdr",
  delta = 1,
  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 results from F-tests, if FALSE t-statistic results from 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
delta	Numeric. Pseudocount zero-correction. Default 1
coeff	Integer. The p-value and log2FoldChange will be associated with this coefficient. Default 2, i.e. the 2. level of the predictor.
allResults	If TRUE will return raw results from the eBayes function
...	Additional arguments for the eBayes and lmFit 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 limma
res <- DA.lia(data = mat, predictor = pred)

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