Generate a Circular Cladogram with Heatmap for Taxa — generate_taxa_cladogram

This function generates a circular cladogram with an integrated heatmap for taxonomic data. It visualizes the phylogenetic relationships between different taxa and their abundances or other coefficients across different taxonomic levels using a tree-like structure (cladogram).

Usage

generate_taxa_cladogram_single(
  data.obj,
  test.list,
  group.var = NULL,
  feature.level,
  feature.mt.method = "none",
  cutoff = 1,
  color.group.level = NULL,
  palette = NULL,
  pdf = FALSE,
  pdf.width = 10,
  pdf.height = 10
)

Arguments

data.obj: A list object in a format specific to MicrobiomeStat, which includes components like feature.tab, feature.ann, meta.dat, etc.
test.list: A list of test results. If NULL, it will be generated using generate_taxa_test_single.
group.var: The name of the grouping variable in meta.dat.
feature.level: A character vector specifying taxonomic levels to be analyzed.
feature.mt.method: Multiple testing method for features, "none" (default), "fdr", or other methods supported by p.adjust.
cutoff: The p-value cutoff for significance.
color.group.level: The taxonomic level used to color-code the branches of the cladogram.
palette: An optional vector of colors to be used for the plot. If NULL, a default color palette will be used.
pdf: Boolean indicating whether to save the plot as a PDF.
pdf.width: The width of the PDF file if saved.
pdf.height: The height of the PDF file if saved.

Value

A ggplot object representing the circular heatmap with phylogenetic tree.

Examples

if (FALSE) { # \dontrun{
data(subset_T2D.obj)

test.list <- generate_taxa_test_single(
    data.obj = subset_T2D.obj,
    time.var = "visit_number",
    t.level = NULL,
    group.var = "subject_race",
    adj.vars = "subject_gender",
    feature.level = c("Phylum", "Class", "Order", "Family", "Genus", "Species"),
    feature.dat.type = "count",
    prev.filter = 0.1,
    abund.filter = 0.0001,
)

plot.list <- generate_taxa_cladogram_single(
  data.obj = subset_T2D.obj,
  test.list = test.list,
  group.var = "subject_gender",
  feature.level = c("Phylum", "Class", "Order", "Family", "Genus", "Species"),
  feature.mt.method = "none",
  cutoff = 0.9,
  color.group.level = "Order"
)

test.list <- generate_taxa_test_single(
    data.obj = subset_T2D.obj,
    time.var = "visit_number",
    t.level = NULL,
    group.var = "subject_race",
    adj.vars = "subject_gender",
    feature.level = c("Order"),
    feature.dat.type = "count",
    prev.filter = 0.1,
    abund.filter = 0.0001,
)

plot.list <- generate_taxa_cladogram_single(
  data.obj = subset_T2D.obj,
  test.list = test.list,
  group.var = "subject_gender",
  feature.level = c("Order"),
  feature.mt.method = "none",
  cutoff = 0.9,
  color.group.level = "Order"
)

data(peerj32.obj)

test.list <- generate_taxa_test_single(
    data.obj = peerj32.obj,
    time.var = "time",
    t.level = NULL,
    group.var = "group",
    adj.vars = "sex",
    feature.level = c("Phylum","Family","Genus"),
    feature.dat.type = "count",
    prev.filter = 0.1,
    abund.filter = 0.0001,
)

plot.list <- generate_taxa_cladogram_single(
  data.obj = peerj32.obj,
  test.list = test.list,
  group.var = "group",
  feature.level = c("Phylum", "Family", "Genus"),
  cutoff = 0.3,
  color.group.level = "Family"
)
} # }