weights_ZINB: weights_ZINB

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

weights_ZINB

Description

Computes the observational weights of the counts under a zero-inflated negative binomial (ZINB) model. For each count, the ZINB distribution is parametrized by three parameters: the mean value and the dispersion of the negative binomial distribution, and the probability of the zero component.

Usage

weights_ZINB(
  object,
  assay_name = "counts",
  design,
  K = 0,
  commondispersion = TRUE,
  zeroinflation = TRUE,
  verbose = FALSE,
  ...
)

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.
design	character name of the metadata columns, formula, or design matrix with rows corresponding to samples and columns to coefficients to be estimated (the user needs to explicitly include the intercept in the design).
K	integer. Number of latent factors.
commondispersion	Whether or not a single dispersion for all features is estimated (default TRUE).
zeroinflation	Whether or not a ZINB model should be fitted. If FALSE, a negative binomial model is fitted instead.
verbose	Print helpful messages.
...	Additional parameters to describe the model, see zinbModel.

Value

A matrix of weights.

See Also

zinbFit for zero-inflated negative binomial parameters' estimation and computeObservationalWeights for weights extraction.

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"))
ps <- phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE),
    phyloseq::sample_data(metadata))
# Calculate the ZINB weights
zinbweights <- weights_ZINB(object = ps, K = 0, design = "~ 1")

mcalgaro93/benchdamic documentation built on March 10, 2024, 10:40 p.m.