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Volcano Plot of Differential Abundance

Source: R/plot_volcano.R
plot_volcano.Rd

Generates a volcano plot to visualize differential abundance results across one or more omics layers.

Usage

plot_volcano(
  expomicset,
  pval_col = "adj.P.Val",
  pval_thresh = 0.05,
  logFC_col = "logFC",
  logFC_thresh = log2(1.5),
  plot_n_sig = TRUE,
  top_n_label = NULL,
  features_to_label = NULL,
  feature_col = "feature",
  xlab = expression(Log[2] * "FC"),
  ylab = expression(-Log[10] * "P"),
  title = "Volcano Plot of Differential Abundance",
  nrow = 2
)

Arguments

expomicset

A MultiAssayExperiment object containing differential abundance results in metadata(expomicset)$differential_abundance.

pval_col

A character string specifying the column containing p-values. Default is "adj.P.Val".

pval_thresh

A numeric threshold for significance. Features with p-values below this are considered significant. Default is 0.05.

logFC_col

A character string specifying the column for log fold changes. Default is "logFC".

logFC_thresh

A numeric threshold for absolute log fold change significance. Default is log2(1.5).

plot_n_sig

Logical; if TRUE, appends the number of significant features to facet titles. Default is TRUE.

top_n_label

Optional integer. If provided, the top n most significant features per assay will be labeled on the plot.

features_to_label

Optional character vector. Specific features to label regardless of significance.

feature_col

A character string naming the feature ID column to use for labeling. Default is "feature".

xlab

Label for the x-axis. Default is expression(Log[2]*"FC").

ylab

Label for the y-axis. Default is expression(-Log[10]*"P").

title

Plot title. Default is "Volcano Plot of Differential Abundance".

nrow

Number of rows in the facet_wrap() layout. Default is 2.

Value

A ggplot2 object representing the volcano plot.

Details

The function:

  • Extracts differential abundance results from metadata(expomicset)$differential_abundance.

  • Assigns each feature a direction of change: Upregulated, Downregulated, or Not-Significant.

  • Uses logFC_thresh and pval_thresh to define thresholds.

  • Adds dashed lines to indicate cutoffs for fold change and significance.

  • Uses facet_wrap() to display each assay (exp_name) separately.

  • Optionally labels the most significant features or user-defined ones.

Examples

# create example data
mae <- make_example_data(
    n_samples = 10,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# perform differential abundance analysis
mae <- run_differential_abundance(
    expomicset = mae,
    formula = ~ smoker + sex,
    abundance_col = "counts",
    method = "limma_voom",
    action = "add"
)
#> Running differential abundance testing.
#> Processing assay: mRNA
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Processing assay: proteomics
#> No group or design set. Assuming all samples belong to one group.
#> tidybulk says: The design column names are "(Intercept), smokeryes, sexM"
#> tidybulk says: to access the raw results (fitted GLM) do `attr(..., "internals")$limma_voom`
#> Differential abundance testing completed.

# create the volcano plot
volcano_p <- mae |>
    plot_volcano()