Use AI software to obtain the hexadecimal color of the color directly from the pdf file of the literature. as follows

"3B99D4", "8ED14B", "F06B49","ECC2F1", "82C7C3", "19413E","1776EB", "F5B2AC", "533085","89363A","19413E", "D92B45", "60C9FF", "1B9F2E", "BA217D", "635019", "E3698A", "076B82", "A86A16"

The color starts from the selected blue as the first color, corresponding clockwise in turn

The following is the piano picture drawn in the same color in the article

Other Distinct Colors

col_vector<-c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', '#008080', '#e6beff', '#9a6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#808080', '#ffffff', '#000000')

library(RColorBrewer)
n <- 60
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
pie(rep(1,n), col=sample(col_vector, n))

library(randomcoloR)
n <- 20
palette <- distinctColorPalette(n)

colors37 = c("#466791","#60bf37","#953ada","#4fbe6c","#ce49d3","#a7b43d","#5a51dc","#d49f36","#552095","#507f2d","#db37aa","#84b67c","#a06fda","#df462a","#5b83db","#c76c2d","#4f49a3","#82702d","#dd6bbb","#334c22","#d83979","#55baad","#dc4555","#62aad3","#8c3025","#417d61","#862977","#bba672","#403367","#da8a6d","#a79cd4","#71482c","#c689d0","#6b2940","#d593a7","#895c8b","#bd5975")

# install.packages("Polychrome")
library(Polychrome)

# create your own color palette based on seedcolors
P36 = createPalette(36,  c("#ff0000", "#00ff00", "#0000ff"))
swatch(P36)

myCol = c("pink1", "violet", "mediumpurple1", "slateblue1", "purple", "purple3",
          "turquoise2", "skyblue", "steelblue", "blue2", "navyblue",
          "orange", "tomato", "coral2", "palevioletred", "violetred", "red2",
          "springgreen2", "yellowgreen", "palegreen4",
          "wheat2", "tan", "tan2", "tan3", "brown",
          "grey70", "grey50", "grey30")

color = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]

use_colors <- c(
  "brown2","deepskyblue2","#46ACC8",
  "#E58601","#B40F20","seagreen","darkgoldenrod2","steelblue",
  "#6bAEd6","#3182BD","#08519C","#fff500","#FE9929","#EC7014","#CC4C02",
  "#8C2D04","#E31A1C","#FA9FB5","#DD3497","#7A0177","#c2e699","#78c679",
  "#006837","#bcbddc","#9e9ac8","#807dba","#6a51a3","#4a1486","#969696",
  "#636363","#252525")

c(p018 = "#E2D200",
  p019 = "#46ACC8",
  p023 = "#E58601",
  p024 = "#B40F20",
  p027 = "#0B775E",
  p028 = "#E1BD6D",
  p029 = "#35274A",
  p030 = "#F2300F",
  p031 = "#7294D4",
  p032 = "#5B1A18",
  p033 = "#9C964A",
  p034 = "#FD6467")

Reference

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Original code

ywliao blog： https://www.cnblogs.com/ywliao/p/12454567.html