Use colorblind-friendly palette for visualization

Use ggplot2 to make nice coordinate, colors, and size with friendly colors.
colors
ggplot2
Author

Guorui Zhong

Published

Friday, December 1, 2023

Modified

Wednesday, January 10, 2024

Color packages

Basic colors

# gray colors
cbp <- c("gray", "lightgray", "darkgray", "black")
barplot(1:4, col = cbp)

# 4 colors
cbp <- c(
  "#b8b8b8", "#02ff00", "#f9a506", "#ff3c31"
)
barplot(1:4, col = cbp)

# 6 colors
cbp <- c(
  "#4C72B0", "#55A868", "#C44E52", "#8172B2", "#CCB974", "#64B5CD"
)
barplot(1:6, col = cbp)

# 8 colors
cbp <- c(
  "#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", 
  "#D55E00", "#CC79A7"
)
barplot(1:8, col = cbp)

# 9 colors
cbp <- c(
    "#000000", "#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", 
    "#0072B2", "#D55E00", "#CC79A7"
)
barplot(1:9, col = cbp)

Color()

load(here("projects", "2023_scRNA_Seurat", "pbmc_tutorial.RData"))
# 657 colors
length(colors())
## [1] 657
show_col(colors()[1:50])

cbp <-c(
  "#80d7e1","#e4cbf2","#ffb7ba","#bf5046","#b781d2","#ece7a3",          "#f5cbe1","#e6e5e3","#d2b5ab","#d9e3f5","#f29432","#9c9895"
)
show_col(cbp)

## Single cell plot
p1 <- DimPlot(pbmc, reduction = "umap",label = T) + NoLegend()
p2 <- DimPlot(pbmc, reduction = "umap",cols = cbp,label = T)+ NoLegend()
p1 + p2

RColorBrewer

library(RColorBrewer)
display.brewer.all()

col <- brewer.pal(9, "Set1")
b1 <- brewer.pal(9, "Set1")
b2 <- brewer.pal(8, "Set2")
mycolor <- c(b1,b2)

ggsci

library(ggsci)
# vignette("ggsci")
DimPlot(pbmc, reduction = "umap", label = TRUE) + 
  scale_color_nejm() +
  NoLegend()

mycolor <- pal_nejm("default", alpha = 0.5)(8)

paletteer

library(paletteer)  
paletteer_c("scico::berlin", n = 10)
## <colors>
## #9EB0FFFF #5AA3DAFF #2D7597FF #194155FF #11181DFF #270C01FF #501802FF #8A3F2AFF #C37469FF #FFACACFF
paletteer_d("RColorBrewer::Paired",n=12)
## <colors>
## #A6CEE3FF #1F78B4FF #B2DF8AFF #33A02CFF #FB9A99FF #E31A1CFF #FDBF6FFF #FF7F00FF #CAB2D6FF #6A3D9AFF #FFFF99FF #B15928FF
paletteer_dynamic("cartography::green.pal", 20)
## <colors>
## #E5F0DAFF #D9E8CEFF #CDE1C2FF #C1D9B6FF #B5D2AAFF #A9CB9FFF #9DC393FF #91BC87FF #85B47BFF #75AA6BFF #65A15CFF #55974CFF #458D3DFF #35832DFF #287721FF #22651DFF #1C5319FF #164116FF #102F12FF #0A1E0FFF

cols4all

# remotes::install_github("mtennekes/cols4all")
library(cols4all)
# c4a_gui()
mycolor <-c4a("light24", 9)
# Color from：Nat Med. 2019 Aug;25(8):1251-1259. 
cbp <-c(
    "#80d7e1","#e4cbf2","#ffb7ba","#bf5046","#b781d2","#ece7a3",
    "#f5cbe1","#e6e5e3","#d2b5ab","#d9e3f5","#f29432","#9c9895"
)
show_col(cbp, labels = TRUE)

DimPlot(pbmc, reduction = "umap", group.by='seurat_clusters', label = T) +
  scale_color_manual(values = cbp)+
  NoLegend()


# Color from：Immunity. 2020 May 19;52(5):808-824.e7.
cbp <- c(
   "#e41e25","#307eb9","#4cb049","#974e9e","#f57f21","#f4ed35",
  "#a65527","#9bc7e0","#b11f2b","#f6b293"
)
show_col(cbp, labels = TRUE)

DimPlot(pbmc, reduction = "umap", group.by='seurat_clusters', label=T) + 
  scale_color_manual(values = cbp)+
  NoLegend()


# Color from：Cell. 2019 Oct 31;179(4):829-845.e20.
cbp <- c(
  "#b38a8f","#bba6a6","#d5b3a5","#e69db8","#c5ae8d","#87b2d4",
  "#babb72","#4975a5","#499994","#8e8786","#93a95d","#f19538",
  "#fcba75","#8ec872","#ad9f35","#8ec872","#d07794","#ff9796",
  "#b178a3","#e56464","#6cb25e","#ca9abe","#d6b54c"
)
show_col(cbp, labels = TRUE)

DimPlot(pbmc, reduction = "umap", group.by='seurat_clusters', label = T) + 
  scale_color_manual(values = cbp) +
  NoLegend()

Pick color from images

# devtools::install_github("joelcarlson/RImagePalette")
library(RImagePalette)
lifeAquatic <- jpeg::readJPEG("color.jpg")
display_image(lifeAquatic)
mycolor <- image_palette(lifeAquatic, n=16) 
show_col(mycolor)