DA_metagenomeSeq: DA_metagenomeSeq

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

DA_metagenomeSeq

Description

Fast run for the metagenomeSeq's differential abundance detection method.

Usage

DA_metagenomeSeq(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  coef = 2,
  norm = "CSS",
  model = "fitFeatureModel",
  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).
design	the model for the count distribution. Can be the variable name, or a character similar to "~ 1 + group", or a formula.
coef	coefficient of interest to grab log fold-changes.
norm	name of the normalization method to use in the differential abundance analysis. Choose the native metagenomeSeq normalization method CSS. Alternatively (only for advanced users), if norm is equal to "TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", or "none" from norm_edgeR, "ratio", "poscounts", or "iterate" from norm_DESeq2, or "TSS" from norm_TSS, the factors are automatically transformed into scaling factors. If custom factors are supplied, make sure they are compatible with metagenomeSeq normalization factors.
model	character equal to "fitFeatureModel" for differential abundance analysis using a zero-inflated log-normal model, "fitZig" for a complex mathematical optimization routine to estimate probabilities that a zero for a particular feature in a sample is a technical zero or not. The latter model relies heavily on the limma package (default model = "fitFeatureModel").
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', the 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

fitZig for the Zero-Inflated Gaussian regression model estimation and MRfulltable for results 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"),
                       "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))
# Calculate the CSS normalization factors
ps_NF <- norm_CSS(object = ps, method = "CSS")
# The phyloseq object now contains the normalization factors:
normFacts <- phyloseq::sample_data(ps_NF)[, "NF.CSS"]
head(normFacts)
# Differential abundance
DA_metagenomeSeq(object = ps_NF, pseudo_count = FALSE, design = ~ group,
    coef = 2, norm = "CSS")

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