Run Differential Abundance Analysis — run_differential

Run a supported differential abundance method on a microbiome_dataset and return a standardized differential_abundance_result object.

Usage

run_differential_abundance(
  object,
  formula,
  group = NULL,
  method = c("ancombc", "ancombc2", "maaslin2"),
  taxonomic_rank = NULL,
  random_effects = NULL,
  coefficient = NULL,
  p_adj_method = "holm",
  prv_cut = 0.1,
  lib_cut = 0,
  struc_zero = FALSE,
  neg_lb = FALSE,
  tol = 1e-05,
  max_iter = 100,
  conserve = FALSE,
  alpha = 0.05,
  global = FALSE,
  n_cl = 1,
  verbose = FALSE,
  normalization = "TSS",
  transform = "LOG",
  analysis_method = "LM",
  standardize = FALSE
)

Arguments

object: A microbiome_dataset object.
formula: Model formula passed to the differential abundance method.
group: Optional grouping variable used by the method.
method: Differential abundance method. Supports "ancombc", "ancombc2", and "maaslin2".
taxonomic_rank: Optional taxonomy rank used to aggregate the microbiome data before modeling.
random_effects: Optional random-effects formula used by methods that support mixed-effects models.
coefficient: Optional coefficient name extracted from the fitted model. When omitted, the first non-intercept coefficient is used.
p_adj_method: Multiple-testing correction method.
prv_cut: Prevalence cutoff passed to ANCOMBC::ancombc().
lib_cut: Library-size cutoff passed to ANCOMBC::ancombc().
struc_zero: Whether to detect structural zeros.
neg_lb: Whether to use the negative lower bound option.
tol: Numerical tolerance passed to ANCOMBC::ancombc().
max_iter: Maximum iteration count passed to ANCOMBC::ancombc().
conserve: Whether to use the conservative variance estimate.
alpha: Significance cutoff passed to ANCOMBC::ancombc().
global: Whether to run the global test.
n_cl: Number of parallel workers passed to ANCOMBC::ancombc().
verbose: Whether to print method progress.
normalization: Normalization method passed to Maaslin2::Maaslin2().
transform: Transformation passed to Maaslin2::Maaslin2().
analysis_method: Modeling backend passed to Maaslin2::Maaslin2().
standardize: Whether to standardize metadata in Maaslin2::Maaslin2().

Value

A differential_abundance_result object.

Examples

data("demo_crossomics", package = "microbiomedataset")

demo_object <- prune_taxa(
  demo_crossomics$microbiome_data,
  variable_id = demo_crossomics$microbiome_data@variable_info$variable_id[1:40]
)

da_result <- run_differential_abundance(
  object = demo_object,
  formula = "study_group",
  method = "ancombc"
)
#> 'ancombc' has been fully evolved to 'ancombc2'. 
#> Explore the enhanced capabilities of our refined method!
#> Loading required package: foreach
#> Loading required package: rngtools
da_result
#> differential_abundance_result
#> Method: ancombc
#> Taxonomic rank: 
#> Group: study_group1
#> Rows: 40