
Alpha Diversity Change Test Per Time Point (Longitudinal)
Source:R/generate_alpha_change_per_time_test_long.R
generate_alpha_change_per_time_test_long.RdPerforms paired tests comparing alpha diversity changes between baseline and each follow-up time point in longitudinal data.
Usage
generate_alpha_change_per_time_test_long(
data.obj,
alpha.obj = NULL,
alpha.name = NULL,
depth = NULL,
time.var,
t0.level,
ts.levels,
subject.var,
group.var,
adj.vars = NULL,
alpha.change.func = "log fold change"
)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_objor importers likemStat_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.
- t0.level
Character or numeric value specifying the baseline time point for longitudinal or paired analyses. Should match a value in the time.var column.
- ts.levels
Character vector specifying the follow-up time points for longitudinal or paired analyses. Should match values in the time.var column.
- 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.
- 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.
- alpha.change.func
Function or method for calculating change in alpha diversity between two timepoints. Options include 'log fold change', 'absolute change', or a custom function.
Value
A list of tables, one for each alpha diversity metric, summarizing the results of the statistical tests.
Examples
if (FALSE) { # \dontrun{
library(vegan)
data("subset_pairs.obj")
alpha_test_results <- generate_alpha_change_per_time_test_long(
data.obj = subset_pairs.obj,
alpha.name = c("shannon", "simpson"),
time.var = "Antibiotic",
t0.level = unique(subset_pairs.obj$meta.dat$Antibiotic)[1],
ts.levels = unique(subset_pairs.obj$meta.dat$Antibiotic)[-1],
subject.var = "MouseID",
group.var = "Sex",
adj.vars = NULL,
alpha.change.func = "absolute change"
)
generate_alpha_per_time_dotplot_long(
data.obj = subset_pairs.obj,
test.list = alpha_test_results,
group.var = "Sex",
time.var = "Antibiotic",
t0.level = unique(subset_pairs.obj$meta.dat$Antibiotic)[1],
ts.levels = unique(subset_pairs.obj$meta.dat$Antibiotic)[-1],
base.size = 16,
theme.choice = "bw"
)
} # }