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visualization + design If you are interested in color, explore my other color tools, Brewer palettes resources, color blindness palettes and math and an exhausting list of 10,000 color names for all those times you couldn't distinguish between tan hide, sea buckthorn, orange peel, west side, sunshade, california and pizzaz.

# Designing for Color blindness

## Color choices and transformations for deuteranopia and other afflictions

Here, I help you understand color blindness and describe a process by which you can make good color choices when designing for accessibility.

The opposite of color blindness is seeing all the colors and I can help you find 1,000 (or more) maximally distinct colors.

You can also delve into the mathematics behind the color blindness simulations and learn about copunctal points (the invisible color!) and lines of confusion.

# Color blindness R code

R code for converting an RGB color for color blindness. For details see the math tab and the resources section for background reading.

$--- title: 'RGB color correction for color blindess: protanopia, deuteranopia, tritanopia' author: 'Martin Krzywinski' web: http://mkweb.bcgsc.ca/colorblind ---${r}
gamma = 2.4
###############################################
# Linear RGB to XYZ
# https://en.wikipedia.org/wiki/SRGB
XYZ = matrix(c(0.4124564, 0.3575761, 0.1804375,
0.2126729, 0.7151522, 0.0721750,
0.0193339, 0.1191920, 0.9503041),
byrow=TRUE,nrow=3)

SA = matrix(c(0.2126,0.7152,0.0722,
0.2126,0.7152,0.0722,
0.2126,0.7152,0.0722),byrow=TRUE,nrow=3)

###############################################
# XYZ to LMS, normalized to D65
# https://en.wikipedia.org/wiki/LMS_color_space
# Hunt, Normalized to D65
LMSD65 = matrix(c( 0.4002, 0.7076, -0.0808,
-0.2263, 1.1653,  0.0457,
0     , 0     ,  0.9182),
byrow=TRUE,nrow=3)
# Hunt, equal-energy illuminants
LMSEQ = matrix(c( 0.38971, 0.68898,-0.07868,
-0.22981, 1.18340, 0.04641,
0      , 0      , 1      ),
byrow=TRUE,nrow=3)
# CIECAM97
SMSCAM97 = matrix(c(  0.8951,  0.2664, -0.1614,
-0.7502,  1.7135,  0.0367,
0.0389, -0.0685,  1.0296),
byrow=TRUE,nrow=3)
# CIECAM02
LMSCAM02 = matrix(c( 0.7328, 0.4296, -0.1624,
-0.7036, 1.6975,  0.0061,
0.0030, 0.0136,  0.9834),
byrow=TRUE,nrow=3)

###############################################
# Determine the color blindness correction in LMS space
# under the condition that the correction does not
# alter the appearance of white as well as
# blue (for protanopia/deuteranopia) or red (for tritanopia).
# For achromatopsia, greyscale conversion is applied
# to the linear RGB values.
getcorrection = function(LMS,type="p",g=gamma) {
red = matrix(c(255,0,0),nrow=3)
blue = matrix(c(0,0,255),nrow=3)
white = matrix(c(255,255,255),nrow=3)
LMSr = LMS %*% XYZ %*% apply(red,1:2,linearize,g)
LMSb = LMS %*% XYZ %*% apply(blue,1:2,linearize,g)
LMSw = LMS %*% XYZ %*% apply(white,1:2,linearize,g)
if(type == "p") {
x = matrix(c(LMSb[2,1],LMSb[3,1],
LMSw[2,1],LMSw[3,1]),byrow=T,nrow=2)
y = matrix(c(LMSb[1,1],LMSw[1,1]),nrow=2)
ab = solve(x) %*% y
C = matrix(c(0,ab[1,1],ab[2,1],0,1,0,0,0,1),byrow=T,nrow=3)
} else if (type == "d") {
x = matrix(c(LMSb[1,1],LMSb[3,1],
LMSw[1,1],LMSw[3,1]),byrow=T,nrow=2)
y = matrix(c(LMSb[2,1],LMSw[2,1]),nrow=2)
ab = solve(x) %*% y
C = matrix(c(1,0,0,ab[1,1],0,ab[2,1],0,0,1),byrow=T,nrow=3)
} else if (type == "t") {
x = matrix(c(LMSr[1,1],LMSr[2,1],
LMSw[1,1],LMSw[2,1]),byrow=T,nrow=2)
y = matrix(c(LMSr[3,1],LMSw[3,1]),nrow=2)
ab = solve(x) %*% y
C = matrix(c(1,0,0,0,1,0,ab[1,1],ab[2,1],0),byrow=T,nrow=3)
} else if (type == "a" | type == "g") {
C = matrix(c(0.2126,0.7152,0.0722,
0.2126,0.7152,0.0722,
0.2126,0.7152,0.0722),byrow=TRUE,nrow=3)
}
return(C)
}

# rgb is a column vector
convertcolor = function(rgb,LMS=LMSD65,type="d",g=gamma) {
C = getcorrection(LMS,type)
if(type == "a" | type == "g") {
T = SA
} else {
M = LMS %*% XYZ
Minv = solve(M)
T = Minv %*% C %*% M
}
print(T)
rgb_converted = T %*% apply(rgb,1:2,linearize,g)
return(apply(rgb_converted,1:2,delinearize,g))
}

# This function implements the method by Vienot, Brettel, Mollon 1999.
# The approach is the same, just the values are different.
# http://vision.psychol.cam.ac.uk/jdmollon/papers/colourmaps.pdf
convertcolor2 = function(rgb,type="d",g=2.2) {
xyz = matrix(c(40.9568, 35.5041, 17.9167,
21.3389, 70.6743, 7.98680,
1.86297, 11.4620, 91.2367),byrow=T,nrow=3)
lms = matrix(c(0.15514, 0.54312, -0.03286,
-0.15514, 0.45684,0.03286,
0,0,0.01608),byrow=T,nrow=3)
rgb = (rgb/255)**g
if(type=="p") {
S = matrix(c(0,2.02344,-2.52581,0,1,0,0,0,1),byrow=T,nrow=3)
rgb = 0.992052*rgb+0.003974
} else if(type=="d") {
S = matrix(c(1,0,0,0.494207,0,1.24827,0,0,1),byrow=T,nrow=3)
rgb = 0.957237*rgb+0.0213814
} else {
stop("Only type p,d defined for this function.")
}
M = lms %*% xyz
T = solve(M) %*% S %*% M
print(T)
rgb = T %*% rgb
rgb = 255*rgb**(1/g)
return(rgb)
}

###############################################
# RGB to Lab
rgb2lab = function(rgb,g=gamma) {
rgb = apply(rgb,1:2,linearize,g)
xyz = XYZ %*% rgb
delta = 6/29
xyz = xyz / (c(95.0489,100,108.8840)/100)
f = function(t) {
if(t > delta**3) {
return(t**(1/3))
} else {
return (t/(3*delta**2) + 4/29)
}
}
L = 116*f(xyz) - 16
a = 500*(f(xyz) - f(xyz))
b = 200*(f(xyz) - f(xyz))
return(matrix(c(L,a,b),nrow=3))
}

# CIE76 (https://en.wikipedia.org/wiki/Color_difference)
deltaE = function(rgb1,rgb2) {
lab1 = rgb2lab(rgb1)
lab2 = rgb2lab(rgb2)
return(sqrt(sum((lab1-lab2)**2)))
}

clip = function(v) {
return(max(min(v,1),0))
}

###############################################
# RGB to/from linear RGB
#https://en.wikipedia.org/wiki/SRGB
linearize = function(v,g=gamma) {
if(v <= 0.04045) {
return(v/255/12.92)
} else {
return(((v/255 + 0.055)/1.055)**g)
}
}

delinearize = function(v,g=gamma) {
if(v <= 0.003130805) {
return(255*12.92*clip(v))
} else {
return(255*clip(1.055*(clip(v)**(1/g))-0.055))
}
}
pretty = function(x) {
noquote(formatC(x,digits=10,format="f",width=9))
}

# a dark red
rgb1 = matrix(c(0,209,253),nrow=3)
# dark green
rgb2 = matrix(c(60,135,0),nrow=3)
# simulate deuteranopia
convertcolor(rgb1,type="d")
convertcolor(rgb2,type="d")
# get color distance before and after simulation
deltaE(rgb1,rgb2)
deltaE(convertcolor(rgb1,type="d"),convertcolor(rgb2,type="d"))
# transformation matrices for each color blindness type
M = LMSD65 %*% XYZ
pretty(solve(M) %*% getcorrection(LMSD65,"p") %*% M)
pretty(solve(M) %*% getcorrection(LMSD65,"d") %*% M)
pretty(solve(M) %*% getcorrection(LMSD65,"t") %*% M)
pretty(SA)
# method by Vienot, Brettel, Mollon, 1999
convertcolor2(rgb1,type="d",g=2.2)
convertcolor2(rgb2,type="d",g=2.2)

$# a dark red rgb1 = matrix(c(225,0,30),nrow=3) # dark green rgb2 = matrix(c(60,135,0),nrow=3) # simulate deuteranopia convertcolor(rgb1,type="d") [,1] [1,] 136.7002 [2,] 136.7002 [3,] 0.0000 convertcolor(rgb2,type="d") [,1] [1,] 116.76071 [2,] 116.76071 [3,] 16.73263 # get color distance before and after simulation deltaE(rgb1,rgb2)  116.9496 deltaE(convertcolor(rgb1,type="d"),convertcolor(rgb2,type="d"))  12.72204 # transformation matrices for each color blindness type M = LMSD65 %*% XYZ pretty(solve(M) %*% getcorrection(LMSD65,"p") %*% M) [,1] [,2] [,3] [1,] 0.1705569911 0.8294430089 0.0000000000 [2,] 0.1705569911 0.8294430089 -0.0000000000 [3,] -0.0045171442 0.0045171442 1.0000000000 pretty(solve(M) %*% getcorrection(LMSD65,"d") %*% M) [,1] [,2] [,3] [1,] 0.3306600735 0.6693399265 -0.0000000000 [2,] 0.3306600735 0.6693399265 0.0000000000 [3,] -0.0278553826 0.0278553826 1.0000000000 pretty(solve(M) %*% getcorrection(LMSD65,"t") %*% M) [,1] [,2] [,3] [1,] 1.0000000000 0.1273988634 -0.1273988634 [2,] -0.0000000000 0.8739092990 0.1260907010 [3,] 0.0000000000 0.8739092990 0.1260907010 pretty(SA) [,1] [,2] [,3] [1,] 0.2126000000 0.7152000000 0.0722000000 [2,] 0.2126000000 0.7152000000 0.0722000000 [3,] 0.2126000000 0.7152000000 0.0722000000 # method by Vienot, Brettel, Mollon, 1999 convertcolor2(rgb1,type="d",g=2.2) [,1] [,2] [,3] [1,] 0.29275003 0.70724967 -2.978356e-08 [2,] 0.29275015 0.70724997 1.232823e-08 [3,] -0.02233659 0.02233658 1.000000e+00 [,1] [1,] 131.81223 [2,] 131.81226 [3,] 36.37274 convertcolor2(rgb2,type="d",g=2.2) [,1] [,2] [,3] [1,] 0.29275003 0.70724967 -2.978356e-08 [2,] 0.29275015 0.70724997 1.232823e-08 [3,] -0.02233659 0.02233658 1.000000e+00 [,1] [1,] 122.71798 [2,] 122.71801 [3,] 48.34316$
news + thoughts

# Cell Genomics cover

Mon 16-01-2023

Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.

Designed in collaboration with with Carlos Urzua. Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).

Browse my gallery of cover designs. A catalogue of my journal and magazine cover designs. (more)

Thu 05-01-2023

My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes. My Science Advances cover that encodes sequence onto hypercubes (volume 9, issue 1, 6 January 2023). (more)

Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9

Browse my gallery of cover designs. A catalogue of my journal and magazine cover designs. (more)

# Regression modeling of time-to-event data with censoring

Mon 21-11-2022

If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber

In a follow-up to our Survival analysis — time-to-event data and censoring article, we look at how regression can be used to account for additional risk factors in survival analysis.

We explore accelerated failure time regression (AFTR) and the Cox Proportional Hazards model (Cox PH). Nature Methods Points of Significance column: Regression modeling of time-to-event data with censoring. (read)

Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Regression modeling of time-to-event data with censoring. Nature Methods 19.

# Music video for Max Cooper's Ascent

Tue 25-10-2022

My 5-dimensional animation sets the visual stage for Max Cooper's Ascent from the album Unspoken Words. I have previously collaborated with Max on telling a story about infinity for his Yearning for the Infinite album.

I provide a walkthrough the video, describe the animation system I created to generate the frames, and show you all the keyframes Frame 4897 from the music video of Max Cooper's Asent.

The video recently premiered on YouTube.

Renders of the full scene are available as NFTs.

# Gene Cultures exhibit — art at the MIT Museum

Tue 25-10-2022

I am more than my genome and my genome is more than me.

The MIT Museum reopened at its new location on 2nd October 2022. The new Gene Cultures exhibit featured my visualization of the human genome, which walks through the size and organization of the genome and some of the important structures. My art at the MIT Museum Gene Cultures exhibit tells shows the scale and structure of the human genome. Pay no attention to the pink chicken.