DA_dearseq: DA_dearseq

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

DA_dearseq

Description

Fast run for dearseq differential abundance detection method.

Usage

DA_dearseq(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  covariates = NULL,
  variables2test = NULL,
  sample_group = NULL,
  test = c("permutation", "asymptotic"),
  preprocessed = FALSE,
  n_perm = 1000,
  verbose = TRUE
)

Arguments

object	a phyloseq or TreeSummarizedExperiment object.
assay_name	the name of the assay to extract from the TreeSummarizedExperiment object (default assayName = "counts"). Not used if the input object is a phyloseq.
pseudo_count	add 1 to all counts if TRUE (default pseudo_count = FALSE).
covariates	a character vector containing the colnames of the covariates to include in the model.
variables2test	a character vector containing the colnames of the variable of interest.
sample_group	a vector of length n indicating whether the samples should be grouped (e.g. paired samples or longitudinal data). Coerced to be a factor. Default is NULL in which case no grouping is performed.
test	a character string indicating which method to use to approximate the variance component score test, either 'permutation' or 'asymptotic' (default test = "permutation").
preprocessed	a logical flag indicating whether the expression data have already been preprocessed (e.g. log2 transformed). Default is FALSE, in which case y is assumed to contain raw counts and is normalized into log(counts) per million.
n_perm	the number of perturbations. Default is 1000
verbose	an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.

Value

A list object containing the matrix of p-values 'pValMat', a matrix of summary statistics for each tag 'statInfo' which are still the p-values as this method does not produce other values, and a suggested 'name' of the final object considering the parameters passed to the function.

See Also

dear_seq for analysis of differential expression/abundance through a variance component test.

Examples

set.seed(1)
# Create a very simple phyloseq object
counts <- matrix(rnbinom(n = 60, size = 3, prob = 0.5), nrow = 10, ncol = 6)
metadata <- data.frame("Sample" = c("S1", "S2", "S3", "S4", "S5", "S6"),
    "group" = as.factor(c("A", "A", "A", "B", "B", "B")))
ps <- phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE),
     phyloseq::sample_data(metadata))
# Differential abundance
DA_dearseq(object = ps, pseudo_count = FALSE, covariates = NULL, 
    variables2test = "group", sample_group = NULL, test = "asymptotic",
    preprocessed = FALSE, verbose = TRUE)

mcalgaro93/benchdamic documentation built on Nov. 28, 2022, 6:55 p.m.