set_dearseq: set_dearseq

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

set_dearseq

Description

Set the parameters for dearseq differential abundance detection method.

Usage

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

Arguments

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
expand	logical, if TRUE create all combinations of input parameters (default expand = TRUE).

Value

A named list containing the set of parameters for DA_dearseq method.

See Also

DA_dearseq

Examples

# Set some basic combinations of parameters for dearseq 
base_dearseq <- set_dearseq(pseudo_count = FALSE, variables2test = "group",
    test = c("permutation", "asymptotic"), expand = TRUE)

mcalgaro93/benchdamic documentation built on Nov. 23, 2024, 5:06 p.m.