Here we are now at the middle of the fourth large part of this talk.get nowheremore quotes
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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.

In an audience of 8 men and 8 women, chances are 50% that at least one has some degree of color blindness1,2. When encoding information or designing content, use colors that is color-blind safe.

1About 8% of males and 0.5% of females are affected with some kind of color blindness in populations of European descent (wikipedia, Worldwide prevalence of red-green color deficiency, JOSAA). The rate for other races is lower Asians and Africans is lower (Caucasian Boys Show Highest Prevalence of Color Blindness Among Preschoolers, AAO).

2The probability that among $N=8$ men and $N=8$ women at least one person is affected by color blindness is $P(men,women) = P(8,8) = 1 - (1-0.08)^8(1-0.005)^8 = 0.51$. For $N=34$ (i.e., 68 people in total), this probability is $P(34,34)=0.95$. Because the rate of color blindness in women is so low, for most groups of mixed gender we can approximate the probability by only counting the men. For example, in a group of 17 women the probability that at least one of them is color blind is $P(0,17) = 0.082$, which is the same probability as for 1 man, $P(1,0)$.

## color receptors are reduced or absent in color blindness

The normal human eye is a 3-channel color detector3. There are three types of photoreceptors, each sensitive to a different part of the spectrum. Their combined response to a given wavelength produces a unique response that is the basis of the perception of color.

3Compared to hearing, the color vision is a primitive detector. While we can hear thousands of distinct frequencies and process them simultaneously, we have only three independent color inputs. While the ear can distinguish pure tones from complex sounds that have multiple frequencies the eye is relatively unsophisticated in separating a color sensation into its three constituent primary stimuli.

People with color blindness have one of the photo receptor groups either reduced in number or entirely missing. With only two groups of photoreceptors, the perception of hue is drastically altered.

For example, in deuteranopia, the most common type of color blindness, the medium (M) wavelength photoreceptors are reduced in number or missing. This results in the loss of perceived difference between reds and greens because only one group of photoreceptors (L) are sensitive to the wavelengths of these colors. The spectrum appears to be split into two hues along the blue-green boundary (see figure below), which is roughly where the photoreceptor sensitivities curves cross.

Each of the three kinds of color blindness are associated with reduced number of each of the three kinds of photoreceptors. In extreme cases, a given type of photoreceptors may be missing. To people with color blindness, objects appear very differently. Artwork is (left) Edvard Munch, Scream (Skrik), 1893, National Gallery, Oslo, Norway (right) Claude Monet, Coquelicots, La promenade (Poppies), 1873, Musée d'Orsay, Paris. Each of the rows in the color ramps on the right show colors that are indistinguishable for each kind of color blindness. (zoom)

Visible light is in the range of 390–700 nm. The exact definition of the upper limit varies, with some sources giving as high as 760 nm. Shorter wavelengths are absorbed by the cornea (<295nm) and lens (315–390nm). Some near infrared light also reaches the retina (760–1,400nm).

## it's all the same to me

The Ishihara test is a color perception test for protanopia and deuteranopia. Think of the Rorschach test, except with a different diagnosis if you can't see a pattern.

Traditionally, the Ishihara test is performed with digits but why not use Mr. Spock4. He knows all the digits and is much more insteresting.

4In tribute to Leonard Nimoy, 1931–2015

The likeness of Mr. Spock drawn using equivalent colors (see image above) for each of the three kinds of color blindness. Image from imagebuddy. (zoom)

## simulating color blindness

Color blindness comes in varying degrees and types. Let's consider total deuternanopia—where the M receptors are missing or completely dysfunctional. Because they only have two kinds of color receptors, someone with this condition will see only two dimensions of color.

To understand how to simulate color blindness we have to look briefly at how color can be represented. You're probaby familiar with the RGB color space—just one kind of many color spaces. The RGB coordinates of a color are a device-dependent output model—they tell a device, such as your monitor or TV how much of a pixel's red, green and blue to activate. Obviously, depending on which specific display panel we're talking about, the output color might actually look very different—it's a function of the actual phosphors and any calibration and adjustments.

It turns out that we can also specify color in terms of coordinates in a space based on the physiological response of the eye to the color. Since a normal eye has three photoreceptors whose sensitivity is centered on short (S), medium (M) and long (L) wavelengths, any given color (i.e. monochromatic light) creates a unique combination of S, M and L cone response.

Using a color's LMS coordinates we can simulate color blindness by modifying the coordinate that corresponds to the missing photoreceptor under the observations that (a) deuteranopes, for example, can distinguish white and greys from blues and greens and (b) colors for which the sensitivity of the missing photoreceptors is low should be perceived normally.

Color blindness can be simulated by considering a color's coordinates in LMS space. (zoom)

Because color blindess reduces the number of color dimensions, a large number of colors distinguishable to people with normal vision appear the same to someone with color blidness. The ramps below show these families of equivalent colors.

Sets of representative hues and tones that are indistinguishable to individuals with different kinds of color blindness. (zoom)

## super color vision

The opposite condition to color blindness exists too—tetrachromacy. In this case, an individual has an extra type of color receptor which improves discrimination in the red part of the spectrum. While the anatomy of their retina can be described, how true tetrachromats subjectively perceive color is unknown. And, perhaps, even unknowable.

Tetrachromacy is common in other animals, such as fish (e.g. goldfish, zebrafish) and birds (e.g. finch, starling). The dimensionality of the perceived color space isn't necessarily proportional to the number of different receptors. If the signal from 3 color receptors are combined by the brain and each processor has a weighted response to a broad range of wavelengths, then a color can be modeled by a point in 3-dimensional space, in which the receptors are the axes. This system can perceive a large number of colors.

In the extreme case where the receptors respond to a very narrow range, of which none overlap with the other, a color is one of three points in a 1-dimensional space. This sytem can perceive only 3 colors.

For example, although the mantis shrimp has 12 different color receptors, the receptors work independently, their color discrimination is poorer than ours.

news + thoughts

# Convolutional neural networks

Thu 17-08-2023

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman

Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.

The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).

Nature Methods Points of Significance column: Convolutional neural networks. (read)

Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.

Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

# Neural network primer

Tue 10-01-2023

Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon

In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.

Nature Methods Points of Significance column: Neural network primer. (read)

Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.

We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

# 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

Thu 17-08-2023

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 19:1513–1515.