Generate Volcano Plots for Longitudinal Taxa Abundance Volatility Test

Usage

generate_taxa_volatility_volcano_long(
  data.obj,
  group.var,
  test.list,
  feature.sig.level = 0.1,
  feature.mt.method = c("fdr", "none"),
  features.plot = NULL,
  palette = c("white", "#7FB695", "#006D2C"),
  pdf = FALSE,
  pdf.wid = 7,
  pdf.hei = 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).
group.var: The grouping variable tested, found in metadata.
test.list: The list of test results returned by generate_taxa_volatility_test_long.
feature.sig.level: The significance level cutoff for highlighting taxa.
feature.mt.method: Multiple testing correction method, "fdr" or "none".
features.plot: A character vector of feature names to be plotted. If NULL, all features will be plotted.
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: Boolean; whether to save the plot as a PDF file.
pdf.wid: Numeric; width of the saved PDF file.
pdf.hei: Numeric; height of the saved PDF file.

Value

A list of ggplot objects of volcano plots for each taxonomic level.

Examples

if (FALSE) { # \dontrun{
data("subset_T2D.obj")
test.list <- generate_taxa_volatility_test_long(
data.obj = subset_T2D.obj,
time.var = "visit_number",
subject.var = "subject_id",
group.var = "subject_race",
adj.vars = "sample_body_site",
prev.filter = 0.1,
abund.filter = 0.0001,
feature.level = c("Order", "Family", "Genus"),
feature.dat.type = "count",
transform = "CLR"
)
plot.list <- generate_taxa_volatility_volcano_long(data.obj = subset_T2D.obj,
                                                   group.var = "subject_race",
                                                   test.list = test.list,
                                                   feature.sig.level = 0.1,
                                                   feature.mt.method = "none")

data("ecam.obj")
test.list <- generate_taxa_volatility_test_long(
  data.obj = ecam.obj,
  time.var = "month_num",
  subject.var = "subject.id",
  group.var = "antiexposedall",
  adj.vars = "delivery",
  prev.filter = 0.1,
  abund.filter = 0.0001,
  feature.level = c("Order", "Family", "Genus"),
  feature.dat.type = "proportion",
  transform = "CLR"
)
plot.list <- generate_taxa_volatility_volcano_long(
  data.obj = ecam.obj,
  group.var = "antiexposedall",
  test.list = test.list,
  feature.sig.level = 0.1,
  feature.mt.method = "none"
)
} # }