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Alpha Diversity Association Test (Single Time Point)

Source: R/generate_alpha_test_single.R
generate_alpha_test_single.Rd

Performs association tests for alpha diversity indices using linear models for cross-sectional data or a single time point.

Usage

generate_alpha_test_single(
  data.obj,
  alpha.obj = NULL,
  alpha.name = NULL,
  depth = NULL,
  time.var = NULL,
  t.level = NULL,
  group.var,
  adj.vars = NULL
)

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.

alpha.obj

A list containing pre-calculated alpha diversity indices. If NULL and alpha diversity is needed, it will be calculated automatically. Names should match the alpha.name parameter (e.g., "shannon", "simpson"). See mStat_calculate_alpha_diversity.

alpha.name

Character vector specifying which alpha diversity indices to analyze. Options include:

  • "shannon": Shannon diversity index

  • "simpson": Simpson diversity index

  • "observed_species": Observed species richness

  • "chao1": Chao1 richness estimator

  • "ace": ACE richness estimator

  • "pielou": Pielou's evenness

  • "faith_pd": Faith's phylogenetic diversity (requires a tree)

depth

Numeric value or NULL. Rarefaction depth for rarefaction workflows. If NULL, uses the minimum sample depth.

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.

t.level

Character string specifying the time level/value to subset data to, if a time variable is provided. Default NULL does not subset data.

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.

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.

Value

A list containing the association tests for each alpha diversity index. Each element in the list corresponds to a different alpha diversity index, and contains a dataframe with the linear model's coefficients, standard errors, t values, and p values.

Examples

if (FALSE) { # \dontrun{
data("subset_T2D.obj")
# Example where alpha diversity indices are calculated beforehand
alpha.obj <- mStat_calculate_alpha_diversity(subset_T2D.obj$feature.tab,
                                             c("shannon", "observed_species", "ace"))
generate_alpha_test_single(data.obj = subset_T2D.obj,
                           alpha.obj = alpha.obj,
                           alpha.name = c("shannon", "observed_species", "ace"),
                           time.var = "visit_number",
                           t.level = NULL,
                           group.var = "subject_race",
                           adj.vars = "subject_gender")

# Example where alpha diversity indices are calculated within the function
generate_alpha_test_single(data.obj = subset_T2D.obj,
                           time.var = "visit_number",
                           t.level = unique(subset_T2D.obj$meta.dat$visit_number)[4],
                           alpha.name = c("shannon", "observed_species"),
                           group.var = "subject_race",
                           adj.vars = "subject_gender")

data("peerj32.obj")
generate_alpha_test_single(data.obj = peerj32.obj,
                           time.var = "time",
                           t.level = "1",
                           alpha.name = c("shannon", "observed_species"),
                           group.var = "group",
                           adj.vars = "sex")
generate_alpha_test_single(data.obj = peerj32.obj,
                           time.var = "time",
                           t.level = "1",
                           alpha.name = c("shannon", "observed_species"),
                           group.var = "group",
                           adj.vars = NULL)
} # }