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And whatever I do will become forever what I've done.Wislawa Szymborskadon't rehearsemore quotes


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


visualization + design

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Cover image for the human genetics special issue. Trends in Genetics October 2012, 28 (10) (lowres, hires, Trends in Genetics)

Creating the Trends in Genetics October 2012 Cover

Lately, I've been making a lot of square things round. So when Rhiannon Macrae, the Editor of Trends in Genetics, requested a Circos-like cover image for the human genetics special edition of the journal, I started drawing circles.

The image was published on the cover of Trends in Genetics human genetics special issue (Trends in Genetics October 2012, 28 (10)).

Tools

Circos, Circos tableviewer, Illustrator CS5, and a cup (or two) of Galileo coffee from a Rancilio Epoca.

Other Covers

Circos has appeared on covers of journals and books. Some of the images were designed by me and others were drawn from papers published in the issue.

Journals

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Cover of Blood, 2 Aug 2012, 120(5). Figure from Egan, J. B., C. X. Shi, et al. (2012). Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides. Blood 120(5): 1060-1066. (Blood)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Genomics, Aug 2012, 100(2). Figure from Katapadi, V. K., M. Nambiar, et al. (2012). Potential G-quadruplex formation at breakpoint regions of chromosomal translocations in cancer may explain their fragility. Genomics 100(2): 72-80. (Genomics)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Science Translational Medicine, December 2010, 2(61). Figure from Lo, Y. M., K. C. Chan, et al. (2010). Maternal plasma DNA sequencing reveals the genome-wide genetic and mutational profile of the fetus. Sci Transl Med 2(61): 61ra91 (Science Translational Medicine)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
EMBO Journal, May 2009, 28(9). Cover design by Martin Krzywinski. (EMBO)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Biotechnology, November 2009, 27(11). Figure from Cho, B. K., K. Zengler, et al. (2009). The transcription unit architecture of the Escherichia coli genome. Nat Biotechnol 27(11): 1043-1049. (Nature Biotechnology)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Genome Research, April 2008, 18(4). Cover design by Ryan Morin (Genome Research)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
American Scientist, September/October 2007. Cover design by Martin Krzywinski — how it was done. (American Scientist)

Books

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
iGenetics, 3rd ed. by Peter Russell (Benjamin Cummings). Cover design by Martin Krzywinski. (iGenetics)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Building Bioinformatics Solutions with Perl, R and MySQL (Oxford University Press). Cover design by Martin Krzywinski. (Building Bioinformatics Solutions)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Designing Universal Knowledge by Gerlinde Schuller (Lars Müller Publishers) (Designing Universal Knowledge)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Chromosomes — art book of film stills, David Cronenberg. Contribution to book design by Martin Krzywinski. (Chromosomes)

source of design

I have a collection of unpublished Circos posters and thought these might be a good starting point. Rhiannon and I narrowed the choice down to the black-and-white design that showed sequenced organisms. We also liked the complex style of a panel of hundreds of Circos images generated with the tableviewer.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
An old Circos poster. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A panel of images generated from the Circos tableviewer. (zoom)

The idea would be that the foreground would be more artistic and stylized, while the background was more technical and complex. I have thousands of images available from the tableviewer (e.g. huge 15,129 image matrix).

Rhiannon also wanted to include the quote by Henry David Thoreau, "Nature and human life are as various as our several constitutions. Who shall say what prospect life offers to another?" This reminded me of a similar but more tragic line from Shakespeare's Julius Caesar, "How many ages hence shall this our lofty scene be acted over in states unborn and accents yet unknown!"

early comps

In the early comps we played around with the idea of using non-genomics elements in the image, such as coins. We thought that we could use the variety of color and shape of the coins to communicate the idea of genetic diversity. However, after wrestling with how to do this effectively the concept was scrapped — the idea of using coins felt both arcane and arbitrary.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
First set of comps. (zoom)

I decided to go with a warm brown color scheme. It's not a color I use a lot of, which makes me think that I should try to do more with it.

Deep brown provides great contrast for saturated colors, though I had to be careful not to make the image look too kitchy with an excess of colour variation. In some of the early comps shown above, two or more different color palettes were used (e.g. grey/red/blue and false color) and this lowered to overall visual cohesion of the image.

It's always a good idea to add variety to design. After all, without any variety we'd be left with a blank page. Ok, so variety is good, but too much variety is very bad, and can make you wish for that blank page again. Think about this: one kind of variety already provides variety! A variety of variety (I run the risk of recursing myself ad infinitum) can not only compete for attention but resonate destructively (that's design-speak for "turn into visual mush").

refining the design

Everyone liked the combination of bright colors and dark background. This is an approach I favour too, which has worked well on other covers.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Experimenting with an organic look. (zoom)

Briefly I experimented with various brush and pencil filters to give the image a more hand-drawn and organic look. Most of the illustrations I generate are very digital — blocks of solid colors and high-contrast shapes — and I thought a departure from this look could work in this case. However, like with the coins, this path didn't produce anything productive.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Refining color palettes. (zoom)

final image elements

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The background is created from a matrix of about 1,400 individual Circos images created by the user community using the tableviewer. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The main element is a Circos image of a 15 x 15 table, also created with the tableviewer. (zoom)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A watermark made up from elements in a tableviewer image that show aggregate statistics for each row and column. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A multi-crop zoom of the main element shown above. This version is colored for punch. (zoom)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Masks showing the locations of smaller vignettes. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
An 8 x 8 tableviewer image with outlined ribbons. (zoom)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Thoreau quote: Nature and human life are as various as our several constitutions. Who shall say what prospect life offers to another? (zoom)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Background and midground elements. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Background and foreground elements. (zoom)

final image

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Final image with all the layers. (Trends in Genetics October 2012, 28 (10)) (zoom)
VIEW ALL

news + thoughts

Machine learning: a primer

Tue 05-12-2017
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.",

...more about the Points of Significance column

Snowflake simulation

Tue 14-11-2017
Symmetric, beautiful and unique.

Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A few of the beautiful snowflakes generated by the Gravner-Griffeath model. (explore)

Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.

Genes that make us sick

Thu 02-11-2017
Where disease hides in the genome.

My illustration of the location of genes in the human genome that are implicated in disease appears in The Objects that Power the Global Economy, a book by Quartz.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The location of genes implicated in disease in the human genome, shown here as a spiral. (more...)

Ensemble methods: Bagging and random forests

Mon 16-10-2017
Many heads are better than one.

We introduce two common ensemble methods: bagging and random forests. Both of these methods repeat a statistical analysis on a bootstrap sample to improve the accuracy of the predictor. Our column shows these methods as applied to Classification and Regression Trees.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Ensemble methods: Bagging and random forests. (read)

For example, we can sample the space of values more finely when using bagging with regression trees because each sample has potentially different boundaries at which the tree splits.

Random forests generate a large number of trees by not only generating bootstrap samples but also randomly choosing which predictor variables are considered at each split in the tree.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Ensemble methods: bagging and random forests. Nature Methods 14:933–934.

Background reading

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

...more about the Points of Significance column

Classification and regression trees

Mon 16-10-2017
Decision trees are a powerful but simple prediction method.

Decision trees classify data by splitting it along the predictor axes into partitions with homogeneous values of the dependent variable. Unlike logistic or linear regression, CART does not develop a prediction equation. Instead, data are predicted by a series of binary decisions based on the boundaries of the splits. Decision trees are very effective and the resulting rules are readily interpreted.

Trees can be built using different metrics that measure how well the splits divide up the data classes: Gini index, entropy or misclassification error.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Classification and decision trees. (read)

When the predictor variable is quantitative and not categorical, regression trees are used. Here, the data are still split but now the predictor variable is estimated by the average within the split boundaries. Tree growth can be controlled using the complexity parameter, a measure of the relative improvement of each new split.

Individual trees can be very sensitive to minor changes in the data and even better prediction can be achieved by exploiting this variability. Using ensemble methods, we can grow multiple trees from the same data.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

Background reading

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

Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.

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: Model Selection and Overfitting. Nature Methods 13:703-704.

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

...more about the Points of Significance column