Add density plot highlight keys

Author: wolski

NEWS.md

@@ -1,5 +1,7 @@
 # prolfqua 1.6.3
 
+- Abundance-density plots now carry per-sample Plotly highlight keys, allowing interactive reports to fade non-hovered
+  sample curves while preserving the existing ggplot output.
 - **Breaking — contrast schema.** The `modelName` column of `get_contrasts()` output is now the selected facade key (`lm`, `rlm`, `rfit`, `lm_impute`, `lm_missing`, `limma`, `limma_impute`, `limma_voom`, `limma_voom_impute`, `limpa`, `deqms`, `deqms_voom`, `firth`, `lmer_nested`, `ropeca_nested`, `firth_nested`, `limpa_nested`) instead of the testing-schema label (`WaldTest_moderated`, `*_DEqMS`, `*_imputed`, …). Rescue/imputation state moved to a new `estimate_type` column with values `observed`, `lod_imputed`, or `missing_fallback`. The redundant `facade` column was removed (its role is now played by `modelName`). The moderated-Wald-test wording belongs in methods text, not per-row labels. Downstream code that filtered on `modelName == "WaldTest_moderated"` (or the `_imputed`/`_DEqMS` variants) or read the `facade` column must migrate to the facade key and `estimate_type`.
 - `build_contrast_analysis()` and the exported `FACADE_REGISTRY` now derive method dispatch and the available-method list from a single seeded registry (`list_facades()`), removing three hand-maintained copies that could drift. New exported base class `ContrastsFacadeBase` holds the shared facade plumbing; the 18 built-in facades are now thin subclasses.
 - `ContrastsPlotter` colours volcano/MA/score plots by `estimate_type` when present (and the colour column was left at its default), keeping LOD-imputed / fallback rows visually distinct now that `modelName` is uniform per run.

R/tidyMS_plotting.R

@@ -226,7 +226,19 @@ plot_intensity_distribution_density <- function(
   legend = NA,
   max_legend_samples = 16
 ) {
-  p <- ggplot(pdata, aes(x = .data[[response]], colour = .data[[sample_name]])) +
+  density_data <- plotly::highlight_key(
+    pdata,
+    key = as.character(pdata[[sample_name]]),
+    group = paste("prolfqua_density", sample_name, sep = "_")
+  )
+  p <- ggplot(
+    density_data,
+    aes(
+      x = .data[[response]],
+      colour = .data[[sample_name]],
+      group = .data[[sample_name]]
+    )
+  ) +
     geom_line(stat = "density")
   if (!is_transformed) {
     p <- p + scale_x_continuous(trans = "log10")

tests/testthat/test-plotting_functions.R

@@ -47,6 +47,28 @@ test_that("density legend is suppressed automatically for many samples", {
   expect_equal(length(p$guides$guides), 1)
 })
 
+test_that("density plot carries sample keys for plotly hover highlighting", {
+  testthat::skip_if_not_installed("plotly")
+
+  pdata <- expand.grid(
+    sample = c("S1", "S2"),
+    feature = seq_len(30),
+    KEEP.OUT.ATTRS = FALSE
+  )
+  pdata$abundance <- c(stats::rnorm(30, mean = 10), stats::rnorm(30, mean = 12))
+
+  p <- plot_intensity_distribution_density(
+    pdata,
+    sample_name = "sample",
+    response = "abundance",
+    is_transformed = TRUE
+  )
+  built <- plotly::plotly_build(plotly::ggplotly(p))
+  trace_keys <- vapply(built$x$data, function(trace) trace$key[[1]], character(1))
+
+  expect_setequal(trace_keys, c("S1", "S2"))
+})
+
 test_that("PCA sample labels repel and are not clipped", {
   matrix <- matrix(stats::rnorm(60), nrow = 10)
   colnames(matrix) <- paste0("sample_", seq_len(6))