DA_metagenomeSeq {benchdamic}

R Documentation

DA_metagenomeSeq

Description

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

Usage

DA_metagenomeSeq(
  object,
  pseudo_count = FALSE,
  design = NULL,
  coef = 2,
  norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none",
    "ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"),
  verbose = TRUE
)

Arguments

object	phyloseq object.
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, or a 'model.matrix' object.
coef	integer or character index vector indicating which coefficients of the linear model are to be tested equal to zero.
norm	name of the normalization method used to compute the scaling factors to use in the differential abundance analysis. If norm is equal to "ratio", "poscounts", or "iterate" the normalization factors are automatically transformed into scaling factors.
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 CSSdefault scaling factors
ps_NF <- norm_CSS(object = ps, method = "default")
# The phyloseq object now contains the scaling factors:
scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.CSSdefault"]
head(scaleFacts)
# Differential abundance
DA_metagenomeSeq(object = ps_NF, pseudo_count = FALSE, design = ~ group,
    coef = 2, norm = "CSSdefault")

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