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[!NOTE] 🎉 biopalette is on CRAN. install.packages("biopalette") installs the current release, 0.2.2.

Overview

biopalette is an R package providing image-inspired color palettes for biomedical visualization.

Every palette begins with a real image — a film still, a scientific figure, an artwork — and is translated into a reproducible color system. The source is always documented: where the colors came from, what they mean, and when to use them.

library(biopalette)

get_palette("babel", n = 5)
get_palette("three_body")
get_palette("walter_white", type = "diverging")

preview_palette("gene_red")
palette_gallery()

# Discrete and continuous ggplot2 scales
scale_color_biopalette("three_body")
scale_fill_biopalette_gradient("mitonuclear_blue")

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Installation

# Current CRAN release
install.packages("biopalette")

# Development version
# remotes::install_github("evanbio/biopalette")
install.packages("biopalette", repos = c('https://evanbio.r-universe.dev', 'https://cloud.r-project.org'))

Requires: R ≥ 4.1.0

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Palettes

Each name links to its source page — source image, color table, and intended use.

Name	Type	Colors	Recommended use	Source
gene_red	Qualitative	2	Emphasized signal versus a dark or neutral counterpart	Better Call Saul — Gene Takavic's red coat
walter_white	Diverging	5	Signed continuous values around a neutral center	Breaking Bad — desert to sky
walter_white2	Qualitative	5	Up to five unordered groups	Breaking Bad — muted earth tones
walter_white3	Diverging	5	Warm-register signed continuous values	Breaking Bad — warm counterpart
babel	Qualitative	21	Many categorical groups with labels or position support	Pan-cancer myeloid atlas (Cell, 2021) — 22 cell types, 21 voices
bcell_atlas	Qualitative	7	Four-to-seven categorical groups with direct labels or position support	Pan-cancer B-cell atlas (Cell, 2024) — graphical abstract
bcell_atlas2	Diverging	5	Signed change between warm and cool biological states	Pan-cancer B-cell atlas (Cell, 2024) — IgA to IgG shift
bcell_clusters	Qualitative	20	Many labeled categorical groups with position or faceting support	Pan-cancer B-cell atlas (Cell, 2024) — Figure 1B cluster legend
three_body	Qualitative	3	Three groups, lineages, or trajectories	Pan-cancer myeloid atlas (Cell, 2021) — three DC trajectories
mitonuclear_blue	Sequential	6	Cool low-to-high continuous values	Mito-nuclear communication in aging (TIBS, 2022) — young blue
mitonuclear_orange	Sequential	6	Warm low-to-high continuous values	Mito-nuclear communication in aging (TIBS, 2022) — aged orange
heat_light	Qualitative	2	Paired categories or experimental conditions	Bond ampholysis (Nature, 2024) — heat and light turn radicals into an ion pair
tam_pastel	Qualitative	6	Four-to-six categorical groups on light backgrounds	Pan-cancer myeloid atlas (Cell, 2021) — soft TAM states
cancer_mosaic	Qualitative	15	Ten-to-fifteen categories with labels or position support	Pan-cancer myeloid atlas (Cell, 2021) — cancer-type mosaic
lactate_steps	Qualitative	5	Five discrete workflow stages or study groups	Lactate metabolism and immunotherapy (JECCR, 2024) — five study stages
fargo	Qualitative	3	Three groups with a dark anchor and restrained warm accent	Fargo — navy, motel-sign teal, and suitcase wine
immune_circuit	Qualitative	8	Four parent groups with paired states	Chondrosarcoma immune-circuit graphical abstract — paired red, blue, green, and purple
immune_circuit_red	Sequential	7	Increasing tumor burden, cytotoxicity, risk, or damage	Chondrosarcoma immune-circuit graphical abstract — tumor coral
immune_circuit_green	Sequential	7	Increasing immune activation, infiltration, or recovery	Chondrosarcoma immune-circuit graphical abstract — T-cell mint
lipid_budding	Qualitative	4	Four categorical groups with a balanced cool–warm structure	Hepatic ER lipid-droplet budding — DGAT2, DGAT1, Seipin, and FIT2
lipid_budding_blue	Sequential	5	Low-to-high continuous values in muted steel blue	Hepatic ER lipid-droplet budding — DGAT2 blue
lipid_budding_rose	Sequential	5	Low-to-high continuous values in dusty rose	Hepatic ER lipid-droplet budding — DGAT1 rose
lipid_budding_orange	Sequential	5	Low-to-high continuous values in salmon orange	Hepatic ER lipid-droplet budding — Seipin orange
lipid_budding_indigo	Sequential	5	Low-to-high continuous values in slate indigo	Hepatic ER lipid-droplet budding — FIT2 indigo
cytokine_sensors	Qualitative	7	Six neuroimmune outcomes with a neutral cytokine anchor	Neuronal cytokine sensing in the CNS (Trends Immunology, 2026)
clone_age	Diverging	5	Age- and state-related change around a neutral center	Clonal hematopoiesis — mutant-clone yellow to HSC purple
clone_memory	Diverging	5	Stimulation, recovery, and clone-state contrasts	Epigenetic HSC memory — activated purple to pre-existing cyan
ppi_obligate	Qualitative	4	Four members of one assembled complex or system	Obligate protein interactions — blue-violet, purple, green, and yellow
ppi_nonspecific	Qualitative	2	Two interaction partners, conditions, or cohorts	Non-specific protein interactions — purple and green
ppi_transient	Diverging	5	Centered interaction scores and signed effects	Transient protein interactions — red through neutral to blue

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From Palette to Figure

biopalette provides the R interface for retrieving and applying image-inspired palettes. Tessera documents their source images, example data, and reproducible R figure recipes. Palette Lab keeps the data and graphical structure fixed while comparing palette behavior across 17 graphical contexts. The overview and panels below are rendered from Palette Lab; Tessera links provide the corresponding figure recipes and data context.

The same palette can behave differently as points, lines, filled regions, heatmap cells, set overlaps, survival curves, or genome-wide signals. These examples show the kinds of figures available through the Tessera recipes and the corresponding palette comparisons in Palette Lab.

Grouped scatter · babel	Multi-line comparison · walter_white2
Correlation heatmap · walter_white	Eye color composition by hair color · lactate_steps
Kaplan–Meier survival curve · heat_light	Manhattan plot · cancer_mosaic

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Function Reference

🎨 Palette Access (4)

• get_palette() — retrieve colors by name, type, and size
• palette_info() — metadata for one named palette
• list_palettes() — data frame of all available palettes
• palette_gallery() — paged visual gallery of all palettes

🔧 Palette Management (3)

• create_palette() — write a new palette to JSON
• remove_palette() — remove a palette by name
• preview_palette() — render color swatches to a plot

📊 ggplot2 Scales (6)

• scale_color_biopalette() / scale_colour_biopalette() — discrete color scales
• scale_fill_biopalette() — discrete fill scales
• scale_color_biopalette_gradient() / scale_colour_biopalette_gradient() — continuous color gradients
• scale_fill_biopalette_gradient() — continuous fill gradients

🔵 Color Utilities (2)

• hex2rgb() — convert HEX to RGB
• rgb2hex() — convert RGB to HEX

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License

MIT License © 2025–2026 Yibin Zhou