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	a character string for the formula. The formula should conform to that used by lm (independent data) or lmer (correlated data). 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	a logical value indicating whether winsorization should be performed to replace outliers (high values). The default is TRUE.
outlier.pct	the expected percentage of outliers. These outliers will be winsorized. The default is 0.03.
zero.handling	a character string of 'pseudo-count' or 'imputation' indicating the zero handling method used when feature.dat is 'count'. If 'pseudo-count', apseudo.cnt will be added to each value in feature.dat. If 'imputation', then we use the imputation approach using the formula in the referenced paper. Basically, zeros are imputed with values proportional to the sequencing depth. When feature.dat is 'proportion', this parameter will be ignored and zeros will be imputed by half of the minimum for each feature.
pseudo.cnt	a positive numeric value for the pseudo-count to be added if zero.handling is 'pseudo-count'. Default is 0.5.
alpha	a numerical value between 0 and 1 indicating the significance level for declaring differential features. Default is 0.05.
p.adj.method	a character string indicating the p-value adjustment approach for addressing multiple testing. 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 March 10, 2024, 10:40 p.m.