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

View source: R/DA.lia.R

DA.liaR Documentation

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.