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Palettome

Hierarchy-aware, interactively editable color palettes for clustered data — built for scRNA-seq clusters/cell subtypes (Seurat, UMAP) but not hard-dependent on Seurat.

LLM was used in this project to speed up the development.

Clusters that belong to the same family / compartment (e.g. subtypes of T cells, subtypes of myeloid cells) can be colored either:

• harmonious — a coordinated palette in a perceptually uniform space. harmonious_style = "sweep" (default) lays the families along one analogous hue arc with a shared monotone lightness ramp and muted chroma (e.g. a smooth blue → purple → rose → tan progression is just one example — a different seed gives a genuinely different arc: green, teal, red, gold, …, not a small jitter around one fixed look — and sweep_anchors lets you specify the exact gradient to sweep through instead of an automatic one); harmonious_style = "per_family" gives each family its own hue and colors its members as graded shades of it.
• contrast — every cluster gets a maximally distinguishable color (greedy max-min selection in Lab space), with family membership still visible through a secondary color channel (the family’s own accent/tag color).

Color choice is color-theory aware even for random seeds: chroma is kept inside the displayable gamut and away from the neon and muddy yellow-green zones, hues are spread with a low-discrepancy sequence, and each hue gets its natural lightness. When neighbor information is supplied, families that are adjacent in the embedding get contrasting hues by default (or analogous, by choice — neighbor_hues), and within a family spatially-adjacent clusters are given far-apart shades for local contrast.

simulate_cvd() and the cvd argument of the plotting functions preview a palette as it appears with color-vision deficiency:

Every generated color can be overridden by hand, and manual overrides survive re-running the generator (new mode, new seed, edited families) until explicitly reset; while manual overrides are in place the remaining colors auto-fit to the lightness/chroma envelope of your picks.

Core vs. UI

Core is fully independent of the UI. as_cluster_data(), detect_families(), generate_palette(), plot_palette_static(), plot_swatches(), export_palette_json()/export_palette_csv(), import_palette_json(), downsample_stratified(), color_distance(), and simulate_cvd() depend only on colorspace, farver, stats, graphics, methods, utils, and jsonlite — all listed in Imports, so a plain install.packages("palettome") (or devtools::load_all()) gives you a fully working package with zero UI packages installed.

The interactive UI is exactly one function, launch_palettome_ui(). It requires shiny, plotly, colourpicker, and shinyjs, all listed under Suggests only. If they aren’t installed, launch_palettome_ui() fails with a message telling you which packages to install and reminding you that every other function still works without them — it never blocks or breaks the rest of the package.

Quick start (core only)

devtools::load_all()  # or library(palettome) once installed

pdata <- as_cluster_data(
  my_df,
  coord_cols = c("UMAP_1", "UMAP_2"),
  cluster_col = "cluster"
)

fam <- detect_families(pdata, k = 4)          # auto-detect via hclust

# fam$neighbors carries centroids (or a connectivity matrix) so families and
# clusters can be placed in space — pass it through for neighbor-aware color
session <- generate_palette(
  fam$assignment,
  mode = "harmonious", harmonious_style = "sweep",
  neighbors = fam$neighbors, seed = 1
)

plot_palette_static(pdata, session)           # ggplot2 if installed, base R otherwise
plot_swatches(session)                        # family-grouped swatch legend
export_palette_json(session, "palette.json")

Key generate_palette() arguments:

To resume editing later, or hand off manual overrides:

session <- set_manual_color(session, cluster = "3", color = "#2E5A87")
# several at once: one color each (or a single color for all); ids not in
# the session are skipped with a warning
session <- set_manual_color(session, cluster = c("4", "5"), color = c("#6E6E6E", "#C9A227"))
session2 <- generate_palette(fam$assignment, session = session, mode = "contrast")
# clusters/families with manual_color == TRUE keep their color unchanged;
# with lightness_range/chroma_range = "auto" the rest fit to the manual picks

optimize_color("#7B00FF", session = session2)  # snap a hand-picked hex into the palette envelope

Different seeds give genuinely different sweep gradients (not a small jitter around one look), or specify the exact gradient yourself:

generate_palette(fam$assignment, mode = "harmonious", seed = 7)  # try a few seeds
generate_palette(fam$assignment, mode = "harmonious",
  sweep_anchors = c("#0B3D91", "#FF5733", "#FFD166"))            # your own gradient

session$clusters/session$families are already plain data frames (see the JSON schema below), but for the common case of just needing the colors themselves – to hand to Seurat::DimPlot(cols = ...), ggplot2::scale_color_manual(values = ...), etc. – use:

cluster_colors(session)                     # named vector: c(c1 = "#4E5716", c2 = "#899925", ...)
cluster_colors(session, format = "data.frame") # data frame: cluster, color
family_colors(session)                      # same, one row per family

Working with a Seurat object

as_cluster_data() has a Seurat method, so you don’t need to hand-extract embeddings/metadata yourself:

pdata <- as_cluster_data(
  seurat_obj,
  reduction = "umap",           # any reduction name (Seurat::Embeddings())
  dims = 1:2,                   # or 1:3 for a 3D embedding
  cluster_col = "seurat_clusters", # any column of seurat_obj[[]]
  family_col = NULL             # optional: a metadata column with an explicit parent/family label
)
# from here on it's identical to the data-frame path:
fam <- detect_families(pdata, k = 4)
session <- generate_palette(fam$assignment, mode = "harmonious", seed = 1)

# hand the result back to Seurat's own plotting:
Seurat::DimPlot(seurat_obj, reduction = "umap", group.by = "seurat_clusters",
                cols = cluster_colors(session))

This method requires the Seurat package (Suggests-only, checked with a friendly error if missing) but nothing else in the package does – everything downstream of as_cluster_data() only ever sees the plain palettome_data structure either input path produces.

Interactive UI

install.packages(c("shiny", "plotly", "colourpicker", "shinyjs"))
session <- launch_palettome_ui(pdata)                    # auto-detects families
session <- launch_palettome_ui(pdata, session = session) # resume a saved session
cluster_colors(session)                                  # your edited colors

launch_palettome_ui() returns the edited session when the app closes – either via the Done – return session to R button or by interrupting R (Esc / Ctrl+C) – so assigning its result keeps every change made in the UI.

The app shows a WebGL (plotly scattergl) scatter plot of the embedding (fixed 1:1 x/y aspect ratio, so distances aren’t visually distorted), a family dendrogram, and a drag-and-drop compartment panel: drag a cluster chip into a different family bin to regroup it, hit + Add compartment to make a new (empty, dashed) bin to drag clusters into, and empty compartments are removed automatically. Mode, harmonious style, neighbor-hue rule, seed, and a colorblind simulation preview are all live. Hit 🎲 Randomize next to the seed for a fresh sweep gradient at a click, or type 2+ comma- separated hex colors into Custom gradient anchors to sweep through your own colors instead of an automatic gradient (a live swatch strip previews what you typed). When the auto light/chroma toggle is switched off, the Lightness- and Chroma-range sliders each get a swatch strip underneath rendered in the current palette’s own hue, dimmed outside the selected sub-range – so the numeric range reads as actual colors, not just two numbers. An R code panel shows the current cluster and family colors as named-vector R code (cluster_colors <- c("3" = "#2E5A87", ...)) with a copy button, ready to paste into a script. Sessions export as JSON/CSV, and the final plot exports as a full-resolution PNG.

Click anywhere on a chip or a family’s whole color bar (not just its label) to open the recolor dialog:

• The hex/swatch picker and three raw HSL sliders (Hue/Saturation/ Lightness – the everyday color-picker model, distinct from the HCL space the generator itself reasons in) both edit the same color and stay in sync either direction; nothing stops you from picking a color the generator would never have chosen – that’s the point of exposing raw controls, and the algorithm won’t fight you on it.
• A live readout underneath always shows both the color’s raw HSL and its perceptual HCL (hue/chroma/lightness) breakdown, so you can see the actual parameters behind whatever’s currently in the box, program-picked or hand-edited.
• Optimize snaps the current color’s lightness/chroma into the palette’s envelope while keeping its hue (calls optimize_color()).
• Recoloring a family immediately reshades its non-manual member clusters around the new hue (in both harmonious styles) – pick a family’s color and its subclusters update automatically, rather than needing a separate “Regenerate colors” click.

The JSON schema (stable contract)

export_palette_json() writes, and import_palette_json() reads, this structure. It is deliberately plain and language-agnostic — a future Python/Scanpy implementation can read and write the exact same file without any R-specific types.

{
  "schema_version": "1.0",
  "clusters": [
    { "cluster": "0", "family_id": "F1", "color": "#3E7CB1", "manual_color": false, "n_cells": 1523 },
    { "cluster": "1", "family_id": "F1", "color": "#7FB1DA", "manual_color": true,  "n_cells": 842 }
  ],
  "families": [
    { "family_id": "F1", "color": "#2C5F8A", "manual_color": false }
  ],
  "params": {
    "mode": "harmonious",
    "harmonious_style": "sweep",
    "sweep_anchors": null,
    "neighbor_hues": "coherent",
    "seed": 1,
    "lightness_range": [34, 82],
    "chroma_range": [24, 46],
    "hue_range": [0, 360],
    "cvd": "none",
    "auto_lightness": true,
    "auto_chroma": true
  }
}

• clusters[].color is the per-cluster fill color actually used for plotting.
• families[].color is the family’s representative/accent color: in harmonious mode it’s a point on the family’s part of the sweep (or the family’s base hue for per_family); in contrast mode it’s the secondary channel that shows family membership even though cluster colors themselves are maximally spread apart.
• manual_color: true marks an entry a person set by hand; generate_palette() never overwrites these unless reset_overrides() is called first.
• n_cells is informational (legend sizing, etc.) and optional/nullable.
• params.lightness_range / chroma_range are always the resolved numeric ranges; auto_lightness / auto_chroma record whether they were derived automatically (so a reload can keep auto-fitting).

CSV export (export_palette_csv()) writes the clusters table (and optionally the families table) as plain CSV with the same column names.

Data input contract

as_cluster_data() accommodates either input into one palettome_data object so every other function only has to know one shape:

• Data frame: as_cluster_data(df, coord_cols = c("UMAP_1","UMAP_2"[,"UMAP_3"]), cluster_col = "cluster", family_col = NULL, cell_id_col = NULL)
• Seurat object: as_cluster_data(seurat_obj, reduction = "umap", dims = 1:2, cluster_col = "seurat_clusters", family_col = NULL) (requires the Seurat package, Suggests-only)

If family_col/an explicit family is supplied, detect_families() uses it directly; otherwise it hierarchically clusters cluster centroids (or a supplied cluster-cluster connectivity/distance matrix) and lets you pick a cut (k or h), with plot_dendrogram() for a preview. Either way, the resulting cluster -> family_id assignment is just a plain data frame you (or the UI’s drag-and-drop panel) can freely rewrite afterward — detection is a starting point, not a final answer.

Performance for large datasets

See inst/benchmark/ for the full write-up. In short: the browser never receives one point per cell for interactive editing. downsample_stratified() precomputes a per-cluster-stratified subsample (default cap 50,000 points) server-side for live dragging/recoloring, and plotly’s WebGL scattergl trace renders that subsample smoothly; a full, un-subsampled render is only produced on final PNG export via plot_palette_static(). Cluster centroids, hierarchy, and per-cluster cell counts are precomputed once (compute_centroids()) so hierarchy edits and recoloring never touch the raw per-cell table at all.

Tests

devtools::test()

Covers: perceptual-distance thresholds and gamut/muddy-zone constraints for contrast-mode selection; the harmonious sweep being a monotone-lightness analogous progression; neighbor_hues switching adjacent families between analogous and contrasting; spatially-adjacent subclusters getting far-apart shades; lightness_range = "auto" fitting to manual picks; optimize_color() preserving hue; and manual overrides surviving generate_palette() re-runs (including across a family reassignment). Plus unit tests for the color-theory primitives (R/color_theory.R) and adjacency helpers (R/adjacency.R).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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