Adding EnhancedVolcano function to the package

[lobanovav]

A quick description: Enhanced Volcano is now a function into the R folder and created a unit test with synthetic data

[Copilot]

Pull request overview

This PR introduces a new Volcano_Plot() function to generate static + interactive volcano plots (via EnhancedVolcano + plotly) and adds a testthat unit test suite using synthetic DEG data.

Changes:

• Added R/Volcano_Plot.R implementing volcano plot generation for one or multiple comparisons, returning plots plus ranked output data.
• Added tests/testthat/test-Volcano_Plot.R covering basic execution, return structure, multiple comparisons, and a few edge cases.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 8 comments.

File	Description
R/Volcano_Plot.R	New exported plotting function using EnhancedVolcano with optional labeling and interactive plotly output.
tests/testthat/test-Volcano_Plot.R	New tests validating Volcano_Plot() output structure and a few edge cases.

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[Copilot]

This function imports and calls EnhancedVolcano::EnhancedVolcano, but the package is not listed in DESCRIPTION (so R CMD check will fail once NAMESPACE is regenerated). Add EnhancedVolcano to Imports (or Suggests + guard usage), and ensure NAMESPACE/man/ are regenerated to include the new @export and imports.

[Copilot]

The interactive plot construction adds a new geom_point() layer on top of p_empty, so points are drawn twice (once from EnhancedVolcano, once from the added layer) and pointSize/other aesthetics may diverge between layers. Consider attaching the text aesthetic to the existing point layer (or modifying/replacing that layer) so the interactive plot doesn’t duplicate geometry and avoids the extra ggplot_build() work.

Suggested change

	# Extract the data used for plotting
	plot_data <- ggplot_build(p_empty)$data[[1]]
	pxx <- p_empty +
	xlab("Fold Change") +
	ylab("Significance") +
	theme_minimal() +
	geom_point(aes(
	text = paste("Gene:", df[[label.col]],
	"<br>Log2FC:", df[[lfc_name]],
	"<br>P-value:", df[[sig_name]]),
	colour = as.character(plot_data$colour),
	fill = as.character(plot_data$colour)
	),
	shape = 21,
	size = 2,
	stroke = 0.1) +
	scale_fill_identity()
	# Add hover text aesthetic directly to the existing plot
	pxx <- p_empty +
	xlab("Fold Change") +
	ylab("Significance") +
	theme_minimal() +
	aes(
	text = paste(
	"Gene:", df[[label.col]],
	"<br>Log2FC:", df[[lfc_name]],
	"<br>P-value:", df[[sig_name]]
	)
	)

[lobanovav]

I need more time to check this suggestion, skipping for now