DA_ANCOM: DA_ANCOM

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

DA_ANCOM

Description

Fast run for ANCOM and ANCOM-BC2 differential abundance detection methods.

Usage

DA_ANCOM(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  fix_formula = NULL,
  adj_formula = NULL,
  rand_formula = NULL,
  lme_control = lme4::lmerControl(),
  contrast = NULL,
  alpha = 0.05,
  p_adj_method = "BH",
  struc_zero = FALSE,
  BC = TRUE,
  n_cl = 1,
  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).
fix_formula	Used when BC = TRUE (ANCOM-BC2). The character string expresses how the microbial absolute abundances for each taxon depend on the fixed effects in metadata.
adj_formula	Used when BC = FALSE (ANCOM). The character string represents the formula for covariate adjustment. Default is NULL.
rand_formula	Optionally used when BC = TRUE or BC = FALSE. The character string expresses how the microbial absolute abundances for each taxon depend on the random effects in metadata. ANCOMB and ANCOM-BC2 follows the lmerTest package in formulating the random effects. See ?lmerTest::lmer for more details. Default is rand_formula = NULL.
lme_control	a list of control parameters for mixed model fitting. See ?lme4::lmerControl for details.
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.
alpha	numeric. Level of significance. Default is 0.05.
p_adj_method	character. method to adjust p-values. Default is "holm". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See ?stats::p.adjust for more details.
struc_zero	logical. Whether to detect structural zeros based on group. Default is FALSE. See Details for a more comprehensive discussion on structural zeros.
BC	boolean for ANCOM method to use. If TRUE the bias correction (ANCOM-BC2) is computed (default BC = TRUE). When BC = FALSE computational time may increase and p-values are not computed.
n_cl	numeric. The number of nodes to be forked. For details, see ?parallel::makeCluster. Default is 1 (no parallel computing).
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. ANCOM (BC = FALSE) does not produce p-values but W statistics. Hence, 'pValMat' matrix is filled with 1 - W / (nfeatures - 1) values which are not p-values. To find DA features a threshold on this statistic can be used (liberal < 0.4, < 0.3, < 0.2, < 0.1 conservative).

See Also

ancombc for analysis of microbiome compositions with bias correction or without it ancom.

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_ANCOM(object = ps, pseudo_count = FALSE, fix_formula = "group", contrast =
   c("group", "B", "A"), verbose = FALSE)

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