DA_linda: DA_linda

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

DA_linda

Description

Fast run for linda differential abundance detection method.

Usage

DA_linda(
  object,
  assay_name = "counts",
  formula = NULL,
  contrast = NULL,
  is.winsor = TRUE,
  outlier.pct = 0.03,
  zero.handling = c("pseudo-count", "imputation"),
  pseudo.cnt = 0.5,
  alpha = 0.05,
  p.adj.method = "BH",
  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.
formula	Character. For example: formula = '~x1*x2+x3+(1|id)'. At least one fixed effect is required.
contrast	character vector with exactly, three elements: a string indicating the name of factor whose levels are the conditions to be compared, the name of the level of interest, and the name of the other level.
is.winsor	Boolean. If TRUE (default), the Winsorization process will be conducted for the OTU table.
outlier.pct	A real value between 0 and 1; Winsorization cutoff (percentile) for the OTU table, e.g., 0.03. Default is NULL. If NULL, Winsorization process will not be conducted.
zero.handling	Character. Specifies the method to handle zeros in the OTU table. Options are "pseudo-count" or "imputation" (default is "pseudo-count"). If "imputation", zeros in the OTU table will be imputed using the formula in the referenced paper. If "pseudo-count", a small constant (pseudo.cnt) will be added to each value in the OTU table.
pseudo.cnt	A positive real value. Default is 0.5. If zero.handling is set to "pseudo-count", this constant will be added to each value in the OTU table.
alpha	A real value between 0 and 1; significance level of differential abundance. Default is 0.05.
p.adj.method	Character; p-value adjusting approach. See R function p.adjust. Default is 'BH'.
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', and a suggested 'name' of the final object considering the parameters passed to the function.

See Also

linda.

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_linda(object = ps, formula = "~ group", contrast = c("group", "B", "A"), 
    is.winsor = TRUE, zero.handling = "pseudo-count", verbose = FALSE)

mcalgaro93/benchdamic documentation built on Nov. 28, 2024, 2:16 p.m.