Paired/Longitudinal Taxa Differential Abundance Test

Performs differential abundance analysis for paired or longitudinal data using linear mixed-effects models via LinDA, accounting for subject-level correlations.

Usage

generate_taxa_test_pair(
  data.obj,
  subject.var,
  time.var = NULL,
  change.base = NULL,
  group.var,
  ref.level = NULL,
  adj.vars = NULL,
  feature.level,
  prev.filter = 0,
  abund.filter = 0,
  feature.dat.type = c("count", "proportion", "other"),
  feature.mt.method = c("fdr", "bonferroni", "none"),
  feature.sig.level = 0.05,
  ...
)

Arguments

data.obj

A MicrobiomeStat data object, which is a list containing at minimum the following components:

• feature.tab: A matrix of feature abundances (taxa/genes as rows, samples as columns)

• meta.dat: A data frame of sample metadata (samples as rows)

Optional components include:

• feature.ann: A matrix/data frame of feature annotations (e.g., taxonomy)

• tree: A phylogenetic tree object (class "phylo")

• feature.agg.list: Pre-aggregated feature tables by taxonomy

Data objects can be created using converters like mStat_convert_phyloseq_to_data_obj or importers like mStat_import_qiime2_as_data_obj.

subject.var

Character string specifying the column name in meta.dat that uniquely identifies each subject or sample unit. Required for longitudinal and paired designs to track repeated measurements.

time.var

Character string specifying the column name in meta.dat containing the time variable. Required for longitudinal and paired analyses. Supports character/factor labels (e.g., "baseline", "week4") and numeric values. Some trend/volatility methods require numeric or coercible-to-numeric time values.

change.base

Value indicating the base/reference level for the time variable. If NULL, the first level is used.

group.var

Character string specifying the column name in meta.dat containing the grouping variable (e.g., treatment, condition, phenotype). Used for between-group comparisons.

ref.level

Character string specifying the reference level for categorical group.var. If NULL, the first level alphabetically is used. Ignored for continuous variables.

adj.vars

Character vector specifying column names in meta.dat to be used as covariates for adjustment in statistical models. These variables will be included as fixed effects.

feature.level

Character vector specifying the taxonomic or annotation level(s) for analysis. Should match column names in feature.ann, such as "Phylum", "Family", "Genus", etc. Use "original" to analyze at the original feature level without aggregation.

prev.filter

Numeric value between 0 and 1. Features with prevalence (proportion of non-zero samples) below this threshold will be excluded from analysis. Default is usually 0 (no filtering).

abund.filter

Numeric value. Features with mean abundance below this threshold will be excluded from analysis. Default is usually 0 (no filtering).

feature.dat.type

Character string specifying the data type of feature.tab. One of:

• "count": Raw count data (will be normalized)

• "proportion": Relative abundance data (should sum to 1 per sample)

• "other": Pre-transformed data (no transformation applied)

feature.mt.method

Character string specifying the multiple-testing correction method. One of "fdr", "bonferroni", or "none".

feature.sig.level

Numeric significance threshold used by the testing procedure.

...

Additional parameters passed to the linda function.

Value

A nested list of differential abundance test results by feature level and comparison.