DA.ds2: DESeq2 with manual geometric means

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

DESeq2 with manual geometric means

Description

Implementation of DESeq2 for DAtest Manual geometric means calculated to avoid errors, see https://github.com/joey711/phyloseq/issues/387

Usage

DA.ds2(
  data,
  predictor,
  paired = NULL,
  covars = NULL,
  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)
out.all	If TRUE, will run "LRT" which will produce one p-value for the predictor. If FALSE will run "Wald" test and will output p-value from one level of the predictor specified by coeff. 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 log2FoldChange (and p-value if test="Wald") will be associated with this coefficient. This coefficient is by default compared to the intercept (1. level of predictor), change this with coeff.ref. Default 2, i.e. the 2. level of the predictor.
coeff.ref	Integer. Reference level of the predictor. Default the intercept, = 1
allResults	If TRUE will return raw results from the DESeq function
...	Additional arguments for the DESeq function

Value

A data.frame with with results.

Examples

# Creating random count_table and predictor
set.seed(4)
mat <- matrix(rnbinom(200, size = 0.1, mu = 500), nrow = 20, ncol = 10)
rownames(mat) <- 1:20
pred <- c(rep("Control", 5), rep("Treatment", 5))

# Running DESeq2
res <- DA.ds2(data = mat, predictor = pred)

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