Generate Alpha Diversity Change Boxplot (Paired) — generate_alpha_change_boxplot

Generates boxplots comparing the change in alpha diversity indices between two time points (paired design), with optional grouping and stratification.

Usage

generate_alpha_change_boxplot_pair(
  data.obj,
  alpha.obj = NULL,
  alpha.name = c("shannon", "observed_species"),
  depth = NULL,
  subject.var,
  time.var,
  group.var = NULL,
  strata.var = NULL,
  adj.vars = NULL,
  change.base = NULL,
  alpha.change.func = c("log fold change"),
  base.size = 16,
  theme.choice = "bw",
  custom.theme = NULL,
  palette = NULL,
  pdf = TRUE,
  file.ann = NULL,
  pdf.wid = 11,
  pdf.hei = 8.5,
  ...
)

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.

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.

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.

strata.var

Character string specifying the column name in meta.dat for stratification. When provided, analyses and visualizations will be performed separately within each stratum (e.g., by site, batch, or sex).

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.

change.base

The base time for calculating the change in alpha diversity.

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 taking two arguments (t, t0).

base.size

Numeric value specifying the base font size for plot text elements. Default is typically 16.

theme.choice

Character string specifying the ggplot2 theme to use. Options include:

"bw": Black and white theme (theme_bw)
"classic": Classic theme (theme_classic)
"gray": Gray theme (theme_gray)
"light": Light theme (theme_light)
"dark": Dark theme (theme_dark)
"minimal": Minimal theme (theme_minimal)
"void": Void theme (theme_void)
"prism": GraphPad Prism-like theme

Can also use a custom ggplot2 theme object via custom.theme.

custom.theme

A custom ggplot2 theme object to override theme.choice. Should be created using ggplot2::theme() or a complete theme function.

palette

Character vector of colors or a named palette for the plot. If NULL, uses default MicrobiomeStat color scheme. Can be:

A vector of color codes (e.g., c("#E41A1C", "#377EB8"))
A palette name recognized by the plotting function

pdf

Logical. If TRUE, saves the plot(s) to PDF file(s) in the current working directory. Default is TRUE.

file.ann

Character string for additional annotation to append to output filenames. Useful for distinguishing multiple outputs.

pdf.wid

Numeric value specifying the width of PDF output in inches. Default is typically 11.

pdf.hei

Numeric value specifying the height of PDF output in inches. Default is typically 8.5.

...

(Optional) Additional arguments to pass to the plotting function.

Value

A boxplot displaying the change in the specified alpha diversity index between two time points, stratified by the specified grouping and/or strata variables (if provided). The boxplot will be saved as a PDF if `pdf` is set to `TRUE`.

Examples

if (FALSE) { # \dontrun{
library(vegan)
data(peerj32.obj)
# Example 1: Both group.var and strata.var are NULL
generate_alpha_change_boxplot_pair(
  data.obj = peerj32.obj,
  alpha.obj = NULL,
  alpha.name = "simpson",
  subject.var = "subject",
  time.var = "time",
  group.var = NULL,
  strata.var = NULL,
  adj.vars = NULL,
  change.base = "1",
  alpha.change.func = "absolute change",
  base.size = 16,
  theme.choice = "bw",
  palette = "lancet",
  pdf = TRUE,
  file.ann = "no_groups",
  pdf.wid = 11,
  pdf.hei = 8.5
)
# Example 2: Only group.var is non-NULL
generate_alpha_change_boxplot_pair(
  data.obj = peerj32.obj,
  alpha.obj = NULL,
  alpha.name = "simpson",
  subject.var = "subject",
  time.var = "time",
  group.var = "group",
  strata.var = NULL,
  adj.vars = NULL,
  change.base = "1",
  alpha.change.func = "log fold change",
  base.size = 16,
  theme.choice = "classic",
  palette = "npg",
  pdf = TRUE,
  file.ann = "group_only",
  pdf.wid = 11,
  pdf.hei = 8.5
)
# Example 3: Both group.var and strata.var are non-NULL
generate_alpha_change_boxplot_pair(
  data.obj = peerj32.obj,
  alpha.obj = NULL,
  alpha.name = "simpson",
  subject.var = "subject",
  time.var = "time",
  group.var = "group",
  strata.var = "sex",
  adj.vars = NULL,
  change.base = "1",
  alpha.change.func = "log fold change",
  base.size = 16,
  theme.choice = "gray",
  palette = "aaas",
  pdf = TRUE,
  file.ann = "group_and_strata",
  pdf.wid = 11,
  pdf.hei = 8.5
)
# Example 4: Both group.var and adj.vars are non-NULL, strata.var is NULL
generate_alpha_change_boxplot_pair(
  data.obj = peerj32.obj,
  alpha.obj = NULL,
  alpha.name = "simpson",
  subject.var = "subject",
  time.var = "time",
  group.var = "group",
  strata.var = NULL,
  adj.vars = c("sex"),
  change.base = "1",
  alpha.change.func = "absolute change",
  base.size = 16,
  theme.choice = "minimal",
  palette = "jama",
  pdf = TRUE,
  file.ann = "group_and_adj",
  pdf.wid = 11,
  pdf.hei = 8.5
)

data("subset_pairs.obj")
generate_alpha_change_boxplot_pair(
  data.obj = subset_pairs.obj,
  alpha.obj = NULL,
  alpha.name = c("simpson"),
  subject.var = "MouseID",
  time.var = "Antibiotic",
  group.var = "Sex",
  strata.var = NULL,
  adj.vars = NULL,
  change.base = "Baseline",
  alpha.change.func = "log fold change",
  base.size = 16,
  theme.choice = "bw",
  palette = "lancet",
  pdf = TRUE,
  file.ann = NULL,
  pdf.wid = 11,
  pdf.hei = 8.5
)
} # }