Twenty — minutes — maybe — more.choose four wordsmore quotes

# biography · fast-forward slide

Martin Krzywinski
Staff Scientist, Bioinformatics
Genome Sciences Centre
BC Cancer Agency
570 W 7th Avenue
Vancouver BC V5Z 4S6

1.604.877.6000 x 673262
martink@bcgsc.ca
@mkrzywinski

# at a glance

EMBO Practical Course: Bioinformatics and Genome Analysis, 5–17 June 2017.

# Choosing, naming and clustering colors—for everyone

## Resources

### Color summarizer

The color summarizer generates statistical color summaries of images.

It reports average RGB, HSV, LAB and LCH color components as well as histograms and individual pixel values for these color spaces. Comes with useful web API for all your automation needs.

Yes! I support LCH, which is extremely useful in generating color ramps and, in general, talking about perceptual aspects of color that are intuitive.

My color summarizer reports the representative colors in an image by grouping colors into clusters of similar colors and reporting the average color in each cluster. This is useful in image identification and comparison.

The color summarizer also identifies representative colors in the image by using k-means clustering to group colors into clusters. The centers of each cluster are also reported by name, using my large database of named colors.

Below is an example of a detailed color report of an image—an adorable Fiat 126p I found while it was screaming out its color against the fading background of Havana.

My color summarizer generates statistical color summaries of images, including a poetic list of words used to describe the colors.

### Adobe Swatches for Brewer Palettes

All the Brewer palettes at a glance.

The Brewer color palettes are an excellent source for perceptually uniform color palettes. I provide Adobe Swatches for all colors in the Brewer Palettes.

I also provide a short talk to help you understand why these palettes are important.

### Color Palettes for Color Blindness

Color blindness is a thing. You should worry about it when you're designing and especially when you're encoding information.

Sets of representative hues and tones that are indistinguishable to individuals with different kinds of color blindness. The rectangle below the each color pair shows how the colors appear to someone with color blindness.

I provide some background on color blindness and give options for choosing 7-, 12- and 15-color palettes that are colorblind safe.

(left) Colors grouped by equivalence of perception in deuteranopes. Each of the two hues is represented in six different brightness and chroma combinations. (right) One of the subsets of colors on the left that are reasonably distinct in both deuteranopia and protanopia. To tritanopes, three of the pairs are difficult to distinguish.

### List of Named Colors

Probably the world's largest list of named colors.

With more than 8,300 colors, even a mantis shrimp would be impressed. You can finally imagine a color you can't even imagine and name it!

Use my list of named colors to name the colors in the Google logo: dodger blue, cinnabar, amber and medium emerland green.

The color name list is hooked into the color summarizer's clustering. You can get a list of words, derived from the color names, that describes an image.

The color summarizer returns words that qualitatively describe the image.

VIEW ALL

# Essentials of Data Visualization—8-part video series

Mon 16-01-2017

In collaboration with the Phil Poronnik and Kim Bell-Anderson at the University of Sydney, I'm delighted to share with you our 8-part video series project about thinking about drawing data and communicating science.

Essentials of Data Visualization: Thinking about drawing data and communicating science.

We've created 8 videos, each focusing on a different essential idea in data visualization: encoding, shapes, color, uncertainty, design, drawing missing or unobserved data, labels and process.

The videos were designed as teaching materials. Each video comes with a slide deck and exercises.

# P values and the search for significance

Mon 16-01-2017
Little P value
What are you trying to say
Of significance?
—Steve Ziliak

We've written about P values before and warned readers about common misconceptions about them, which are so rife that the American Statistical Association itself has a long statement about them.

This month is our first of a two-part article about P values. Here we look at 'P value hacking' and 'data dredging', which are questionable practices that invalidate the correct interpretation of P values.

Nature Methods Points of Significance column: P values and the search for significance. (read)

We also illustrate how P values can lead us astray by asking "What is the smallest P value we can expect if the null hypothesis is true but we have done many tests, either explicitly or implicitly?"

Incidentally, this is our first column in which the standfirst is a haiku.

Altman, N. & Krzywinski, M. (2017) Points of Significance: P values and the search for significance. Nature Methods 14:3–4.

Krzywinski, M. & Altman, N. (2013) Points of significance: Significance, P values and t–tests. Nature Methods 10:1041–1042.

# Intuitive Design

Thu 03-11-2016

Appeal to intuition when designing with value judgments in mind.

Figure clarity and concision are improved when the selection of shapes and colors is grounded in the Gestalt principles, which describe how we visually perceive and organize information.

One of the Gestalt principles tells us that the magenta and green shapes will be perceived as as two groups, overriding the fact that the shapes within the group might be different. What the principle does not tell us is how the reader is likely to value each group. (read)

The Gestalt principles are value free. For example, they tell us how we group objects but do not speak to any meaning that we might intuitively infer from visual characteristics.

Nature Methods Points of View column: Intuitive Design. (read)

This month, we discuss how appealing to such intuitions—related to shapes, colors and spatial orientation— can help us add information to a figure as well as anticipate and encourage useful interpretations.

Krzywinski, M. (2016) Points of View: Intuitive Design. Nature Methods 13:895.

# Regularization

Fri 04-11-2016

Constraining the magnitude of parameters of a model can control its complexity.

This month we continue our discussion about model selection and evaluation and address how to choose a model that avoids both overfitting and underfitting.

Ideally, we want to avoid having either an underfitted model, which is usually a poor fit to the training data, or an overfitted model, which is a good fit to the training data but not to new data.

Nature Methods Points of Significance column: Regularization (read)

Regularization is a process that penalizes the magnitude of model parameters. This is done by not only minimizing the SSE, $\mathrm{SSE} = \sum_i (y_i - \hat{y}_i)^2$, as is done normally in a fit, but adding to this minimized quantity the sum of the mode's squared parameters, $\mathrm{SSE} + \lambda \sum_i \hat{\beta}^2_i$.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.

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

# Model Selection and Overfitting

Fri 04-11-2016

With four parameters I can fit an elephant and with five I can make him wiggle his trunk. —John von Neumann.

By increasing the complexity of a model, it is easy to make it fit to data perfectly. Does this mean that the model is perfectly suitable? No.

When a model has a relatively large number of parameters, it is likely to be influenced by the noise in the data, which varies across observations, as much as any underlying trend, which remains the same. Such a model is overfitted—it matches training data well but does not generalize to new observations.

Nature Methods Points of Significance column: Model Selection and Overfitting (read)

We discuss the use of training, validation and testing data sets and how they can be used, with methods such as cross-validation, to avoid overfitting.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.