One function for all taxa/feature visualizations. Automatically detects study design (cross-sectional, longitudinal, or paired) and routes to the appropriate underlying function.
Usage
plot_taxa(
data.obj,
plot.type = c("barplot", "boxplot", "heatmap", "dotplot", "areaplot", "spaghettiplot",
"cladogram"),
subject.var = NULL,
time.var = NULL,
group.var = NULL,
strata.var = NULL,
time.points = NULL,
feature.level = "original",
feature.select = 20,
feature.dat.type = c("count", "proportion", "other"),
change.type = c("none", "relative", "log_fold", "absolute"),
prev.filter = 0,
abund.filter = 0,
transform = c("identity", "sqrt", "log"),
theme = "bw",
...
)Arguments
- data.obj
A MicrobiomeStat data object containing feature.tab, meta.dat, and optionally feature.ann, tree, and feature.agg.list.
- plot.type
Type of visualization: - "barplot": Stacked bar plot of relative abundances - "boxplot": Box plots comparing groups/time points - "heatmap": Heatmap of feature abundances - "dotplot": Dot plot with effect sizes - "areaplot": Stacked area plot (longitudinal only) - "spaghettiplot": Individual trajectories (longitudinal only) - "cladogram": Phylogenetic cladogram (single time point only)
- subject.var
Character. Name of the subject/sample ID variable in meta.dat. Required for longitudinal and paired designs.
- time.var
Character. Name of the time variable in meta.dat. NULL for cross-sectional studies.
- group.var
Character. Name of the grouping variable (e.g., treatment, condition).
- strata.var
Character. Name of the stratification variable for faceting.
- time.points
Time point specification. Can be: - NULL: use all available time points (auto-detect design) - Single value: cross-sectional at that time point - Vector of 2: paired design (baseline, followup) - Vector of >2: longitudinal with specific time points - List: list(baseline = "T0", followup = c("T1", "T2"))
- feature.level
Character vector. Taxonomic level(s) for aggregation. Options include column names from feature.ann or "original" for no aggregation.
- feature.select
Feature selection. Can be: - Integer: top N features by mean abundance (default: 20) - Character vector: specific feature names to display - NULL: use function defaults
- feature.dat.type
Data type: "count", "proportion", or "other".
- change.type
For paired/longitudinal comparisons: - "none": show raw values (default) - "relative": relative change (x1-x0)/(x1+x0) - "log_fold": log2 fold change - "absolute": absolute difference
- prev.filter
Numeric. Minimum prevalence threshold (0-1) for filtering.
- abund.filter
Numeric. Minimum abundance threshold for filtering.
- transform
Transformation for visualization: "identity", "sqrt", or "log".
- theme
Theme specification. Can be: - Character: preset name ("bw", "classic", "minimal", "prism") - List: list(base.size = 12, choice = "bw", palette = NULL)
- ...
Additional arguments passed to the underlying function.
Examples
if (FALSE) { # \dontrun{
data(peerj32.obj)
# Cross-sectional barplot
plot_taxa(peerj32.obj, "barplot",
group.var = "group",
feature.level = "Phylum")
# Longitudinal boxplot with change
plot_taxa(peerj32.obj, "boxplot",
subject.var = "subject",
time.var = "time",
group.var = "group",
change.type = "relative")
# Paired heatmap
plot_taxa(peerj32.obj, "heatmap",
subject.var = "subject",
time.var = "time",
time.points = c("1", "2"),
feature.level = "Genus")
} # }