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Generate spaghetti plots of beta diversity over time

Source: R/generate_beta_pc_spaghettiplot_long.R
generate_beta_pc_spaghettiplot_long.Rd

This function generates spaghetti plots of beta diversity metrics over time for longitudinal microbiome studies. It allows plotting principal coordinates from different dimension reduction methods such as PCoA, NMDS, tSNE or UMAP. By default it uses PCoA for dimension reduction.

Usage

generate_beta_pc_spaghettiplot_long(
  data.obj = NULL,
  dist.obj = NULL,
  pc.obj = NULL,
  pc.ind = c(1, 2),
  subject.var,
  time.var,
  t0.level = NULL,
  ts.levels = NULL,
  group.var = NULL,
  strata.var = NULL,
  adj.vars = NULL,
  dist.name = c("BC"),
  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 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.

dist.obj

Distance matrix between samples, usually calculated using mStat_calculate_beta_diversity function. If NULL, beta diversity will be automatically computed from data.obj using mStat_calculate_beta_diversity.

pc.obj

A list containing the results of dimension reduction/Principal Component Analysis. This should be the output from functions like mStat_calculate_PC, containing the PC coordinates and other metadata. If NULL (default), dimension reduction will be automatically performed using metric multidimensional scaling (MDS) via mStat_calculate_PC. The pc.obj list structure should contain:

points

A matrix with samples as rows and PCs as columns containing the coordinates.

eig

Eigenvalues for each PC dimension.

vectors

Loadings vectors for features onto each PC.

Other metadata

like method, dist.name, etc.

See mStat_calculate_PC function for details on output format.

pc.ind

Numeric vector indicating which Principal Coordinates to plot.

subject.var

Character string indicating the variable for subject identifiers.

time.var

Character string indicating the variable for time points.

t0.level

Character or numeric, baseline time point for longitudinal analysis, e.g. "week_0" or 0. Required.

ts.levels

Character vector, names of follow-up time points, e.g. c("week_4", "week_8"). Required.

group.var

Character string indicating the variable for group identifiers.

strata.var

Character string indicating the variable for stratum identifiers.

adj.vars

A character vector containing the names of the columns in data.obj$meta.dat to include as covariates in the PERMANOVA analysis. If no covariates are needed, use NULL (default).

dist.name

Vector of character strings indicating the distance measures to include in the plot.

base.size

Numeric value indicating the base font size for the plot.

theme.choice

Plot theme choice. Specifies the visual style of the plot. Can be one of the following pre-defined themes: - "prism": Utilizes the ggprism::theme_prism() function from the ggprism package, offering a polished and visually appealing style. - "classic": Applies theme_classic() from ggplot2, providing a clean and traditional look with minimal styling. - "gray": Uses theme_gray() from ggplot2, which offers a simple and modern look with a light gray background. - "bw": Employs theme_bw() from ggplot2, creating a classic black and white plot, ideal for formal publications and situations where color is best minimized. - "light": Implements theme_light() from ggplot2, featuring a light theme with subtle grey lines and axes, suitable for a fresh, modern look. - "dark": Uses theme_dark() from ggplot2, offering a dark background, ideal for presentations or situations where a high-contrast theme is desired. - "minimal": Applies theme_minimal() from ggplot2, providing a minimalist theme with the least amount of background annotations and colors. - "void": Employs theme_void() from ggplot2, creating a blank canvas with no axes, gridlines, or background, ideal for custom, creative plots. Each theme option adjusts various elements like background color, grid lines, and font styles to match the specified aesthetic. Default is "bw", offering a universally compatible black and white theme suitable for a wide range of applications.

custom.theme

A custom ggplot theme provided as a ggplot2 theme object. This allows users to override the default theme and provide their own theme for plotting. Custom themes are useful for creating publication-ready figures with specific formatting requirements.

To use a custom theme, create a theme object with ggplot2::theme(), including any desired customizations. Common customizations for publication-ready figures might include adjusting text size for readability, altering line sizes for clarity, and repositioning or formatting the legend. For example:

“`r my_theme <- ggplot2::theme( axis.title = ggplot2::element_text(size=14, face="bold"), # Bold axis titles with larger font axis.text = ggplot2::element_text(size=12), # Slightly larger axis text legend.position = "top", # Move legend to the top legend.background = ggplot2::element_rect(fill="lightgray"), # Light gray background for legend panel.background = ggplot2::element_rect(fill="white", colour="black"), # White panel background with black border panel.grid.major = ggplot2::element_line(colour = "grey90"), # Lighter color for major grid lines panel.grid.minor = ggplot2::element_blank(), # Remove minor grid lines plot.title = ggplot2::element_text(size=16, hjust=0.5) # Centered plot title with larger font ) “`

Then pass `my_theme` to `custom.theme`. If `custom.theme` is NULL (the default), the theme is determined by `theme.choice`. This flexibility allows for both easy theme selection for general use and detailed customization for specific presentation or publication needs.

palette

An optional parameter specifying the color palette to be used for the plot. It can be either a character string specifying the name of a predefined palette or a vector of color codes in a format accepted by ggplot2 (e.g., hexadecimal color codes). Available predefined palettes include 'npg', 'aaas', 'nejm', 'lancet', 'jama', 'jco', and 'ucscgb', inspired by various scientific publications and the `ggsci` package. If `palette` is not provided or an unrecognized palette name is given, a default color palette will be used. Ensure the number of colors in the palette is at least as large as the number of groups being plotted.

pdf

Logical indicating whether to save the plot as a PDF file.

file.ann

Optional character string to append to the file name.

pdf.wid

Numeric value indicating the width of the PDF file.

pdf.hei

Numeric value indicating the height of the PDF file.

...

Additional arguments to be passed to the plotting function.

Value

A list of ggplot objects for each distance measure and Principal Coordinate.

Examples

if (FALSE) { # \dontrun{

# Load required libraries and data
library(vegan)

# Example with ecam.obj dataset
data(ecam.obj)
dist.obj <- mStat_calculate_beta_diversity(ecam.obj, "BC")
pc.obj <- mStat_calculate_PC(dist.obj)
generate_beta_pc_spaghettiplot_long(
  data.obj = ecam.obj,
  dist.obj = NULL,
  pc.obj = NULL,
  subject.var = "studyid",
  time.var = "month",
  t0.level = "0",
  ts.levels = as.character(sort(as.numeric(unique(ecam.obj$meta.dat$month))))[2:10],
  group.var = "diet",
  strata.var = "delivery",
  adj.vars = NULL,
  dist.name = c('BC'),
  base.size = 20,
  theme.choice = "bw",
  custom.theme = NULL,
  palette = NULL,
  pdf = TRUE,
  file.ann = NULL,
  pdf.wid = 11,
  pdf.hei = 8.5
)
generate_beta_pc_spaghettiplot_long(
  data.obj = ecam.obj,
  dist.obj = NULL,
  pc.obj = NULL,
  subject.var = "studyid",
  time.var = "month",
  t0.level = "0",
  ts.levels = as.character(sort(as.numeric(unique(ecam.obj$meta.dat$month))))[2:10],
  group.var = "diet",
  strata.var = NULL,
  adj.vars = NULL,
  dist.name = c('BC'),
  base.size = 20,
  theme.choice = "bw",
  custom.theme = NULL,
  palette = NULL,
  pdf = TRUE,
  file.ann = NULL,
  pdf.wid = 11,
  pdf.hei = 8.5
)
# Example with peerj32.obj dataset
data(peerj32.obj)
generate_beta_pc_spaghettiplot_long(
  data.obj = peerj32.obj,
  dist.obj = NULL,
  pc.obj = NULL,
  subject.var = "subject",
  time.var = "time",
  t0.level = "1",
  ts.levels = "2",
  group.var = "group",
  strata.var = "sex",
  adj.vars = NULL,
  dist.name = c('BC'),
  base.size = 20,
  theme.choice = "bw",
  custom.theme = NULL,
  palette = NULL,
  pdf = TRUE,
  file.ann = NULL,
  pdf.wid = 11,
  pdf.hei = 8.5
)
generate_beta_pc_spaghettiplot_long(
  data.obj = peerj32.obj,
  dist.obj = NULL,
  pc.obj = NULL,
  subject.var = "subject",
  time.var = "time",
  t0.level = "1",
  ts.levels = "2",
  group.var = "group",
  strata.var = NULL,
  adj.vars = NULL,
  dist.name = c('BC'),
  base.size = 20,
  theme.choice = "bw",
  custom.theme = NULL,
  palette = NULL,
  pdf = TRUE,
  file.ann = NULL,
  pdf.wid = 11,
  pdf.hei = 8.5
)
} # }