biopalette

Image-Inspired Color Palettes for Biomedical Visualization

[!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.

Installation

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

# Development version
remotes::install_github("evanbio/biopalette")

Quick Start

library(biopalette)

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

preview_palette("gene_red")
palette_gallery()

scale_color_biopalette("three_body")
scale_fill_biopalette_gradient("mitonuclear_blue")

Palettes

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)
`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)
`heat_light`	Qualitative	2	Paired categories or experimental conditions	Bond ampholysis (Nature, 2024)
`tam_pastel`	Qualitative	6	Four-to-six categorical groups on light backgrounds	Pan-cancer myeloid atlas (Cell, 2021)
`cancer_mosaic`	Qualitative	15	Ten-to-fifteen categories with labels or position support	Pan-cancer myeloid atlas (Cell, 2021)
`lactate_steps`	Qualitative	5	Five discrete workflow stages or study groups	Lactate metabolism and immunotherapy (JECCR, 2024)
`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
`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
`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

Function Areas

Area	Functions
Palette access	`get_palette()`, `palette_info()`, `list_palettes()`, `palette_gallery()`
Palette management	`create_palette()`, `remove_palette()`, `preview_palette()`
ggplot2 scales	`scale_color_biopalette()`, `scale_fill_biopalette()`, and gradient variants
Color utilities	`hex2rgb()`, `rgb2hex()`

Documentation

Function Reference

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.

Palette Lab overview showing representative graphical contexts

The panels below are rendered from Palette Lab with fixed data and figure structure. Each Tessera link provides the corresponding reproducible figure recipe and data context.

_{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}