Longitudinal Alpha Diversity Volatility Test in MicrobiomeStat — generate_alpha_volatility_test

This function, part of the MicrobiomeStat package, calculates the volatility of alpha diversity measures in longitudinal data and tests the association between the volatility and a group variable. Volatility is calculated as the mean of absolute differences between consecutive alpha diversity measures, normalized by the time difference (mean(abs(alpha_j - alpha_{j-1}) / (t_j - t_{j-1}))).

Usage

generate_alpha_volatility_test_long(
  data.obj,
  alpha.obj = NULL,
  alpha.name = c("shannon", "observed_species"),
  depth = NULL,
  time.var,
  subject.var,
  group.var,
  adj.vars = NULL
)

Arguments

data.obj: A list object in a format specific to MicrobiomeStat, which can include components such as feature.tab (matrix), feature.ann (matrix), meta.dat (data.frame), tree, and feature.agg.list (list). The data.obj can be converted from other formats using several functions from the MicrobiomeStat package, including: 'mStat_convert_DGEList_to_data_obj', 'mStat_convert_DESeqDataSet_to_data_obj', 'mStat_convert_phyloseq_to_data_obj', 'mStat_convert_SummarizedExperiment_to_data_obj', 'mStat_import_qiime2_as_data_obj', 'mStat_import_mothur_as_data_obj', 'mStat_import_dada2_as_data_obj', and 'mStat_import_biom_as_data_obj'. Alternatively, users can construct their own data.obj. Note that not all components of data.obj may be required for all functions in the MicrobiomeStat package.
alpha.obj: An object containing precomputed alpha diversity indices, typically calculated by the function `mStat_calculate_alpha_diversity`. If NULL (the default), alpha diversity will be calculated from the data.obj using `mStat_calculate_alpha_diversity`.
alpha.name: A string with the name of the alpha diversity index to compute. Options could include: "shannon", "simpson", "observed_species", "chao1", "ace", and "pielou".
depth: An integer specifying the sequencing depth for the "Rarefy" and "Rarefy-TSS" methods. If NULL, no rarefaction is performed.
time.var: A string representing the time variable's name in the metadata. The default is NULL.
subject.var: A string indicating the variable for subject identifiers.
group.var: A string representing the group variable's name in the metadata.
adj.vars: A character vector with the names of adjustment variables in the metadata, used in fitting the linear model for residuals.

Value

A summary object containing the results of the linear model testing the association between alpha diversity volatility and the group variable.

Details

The function first obtains the residuals by fitting a linear model with alpha diversity as the response and adjustment variables as predictors. It then calculates volatility for each subject and tests its association with a group variable using a linear model. If the group variable specified in `group.var` has more than two levels, an ANOVA is performed to test the association between alpha diversity volatility and the group variable.

Examples

if (FALSE) { # \dontrun{
data("subset_T2D.obj")
generate_alpha_volatility_test_long(
data.obj = subset_T2D.obj,
alpha.obj = NULL,
alpha.name = c("shannon","observed_species"),
time.var = "visit_number_num",
subject.var = "subject_id",
group.var = "subject_race",
adj.vars = NULL
)
generate_alpha_volatility_test_long(
data.obj = subset_T2D.obj,
alpha.obj = NULL,
alpha.name = c("shannon","observed_species"),
time.var = "visit_number_num",
subject.var = "subject_id",
group.var = "subject_race",
adj.vars = "subject_gender"
)
} # }