Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - contact me Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca on Twitter Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Lumondo Photography Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Pi Art Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Hilbertonians - Creatures on the Hilbert Curve
Safe, fallen down this way, I want to be just what I am.Cocteau Twinssafe at lastmore quotes

curves: fun



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


art + design

Math geek? If you like the clean geometric design of the type posters, you may enjoy something even more mathematical. Design that transcends repetition: Art of Pi, Phi and e posters.

Visions of Type

typography and bird songs

Consider the fact that, if you live in a city, birds are essentially the only wildlife that you meet during your day.

Depending on where you live, you might come several species without even trying. In Vancouver, on my short 10 minute walk to work, I have a good chance to see rock doves, crows, mallars, wigeons, hooded mergansers (if I'm lucky), house sparrows, song sparrows, red-winged black birds, white-crowned sparrows, bushtits, black-capped chickadees, northern flickers, and the mother-of-all-honkers: Canada geese.

Birds and letters are everywhere—art of nature and man.

Letter forms, on the other hand, are the art that is also everywhere. Every typeface is an artistic expression.

Regardless where you live, sadly, you are likely to come across mutants like Comic Sans, Arial and Times New Roman. Hideous creatures from the shallows. Try to find Gotham, Gill Sans, Frutiger, or Garamond.

learning bird songs

Mnemonics of bird songs help you remember the call and recognize the bird. It's so much easier to think "Quick, three beers!" — the call of the Olive-sided flycatcher — rather than "Chirp, chirp, chirp."

The mnemonic captures the cadence and repetition scheme of the song.

For example, if you listen to the white-throated sparrow you can't help but think that this little guy is trying to tell us something.

the mnemonics

French Zonotrichia albicollis: Baisse ta jupe, Philomène, Philomène, Philomène. How differently we hear!
—Madelaine Lemieux (via Twitter)

Dear sweet Canada Canada Canada.
White-throated Sparrow (Zonotrichia albicollis)

Potato chip!
American Goldfinch (Spinus tristis)

Here here. Come right here, dear.
Baltimore Oriole (Icterus galbula)

Who cooks for you?
Barred Owl (Strix varia)

Fire fire. Where where? Here here! See it, see it.
Indigo Bunting (Passerina cyanea)

Clear. Wick, wick, wick.
Northern Flicker (Colaptes auratus)

Quick, three beers!
Olive-sided Flycatcher (Contopus cooperi)

Where are you? Here I am.
Red-eyed Vireo (Vireo olivaceus)

Chubby chubby cheeks. Chubby cheeks.
Ruby-crowned kinglet (Regulus calendula)

Here sweetie.
Black-capped chickadee (Poecile atricapillus)

See me, pretty, pretty me.
White-crowned sparrow (Zonotrichia leucophrys)

the posters

If you love birds and typography, these posters are for you.

The mnemonic for the bird's song is presented on a background that proportionally presents the bird's plumage colors.

If you explore the posters, you just might find the bird too.


Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Dear sweet Canada Canada Canada. — song of the White-throated Sparrow (Zonotrichia albicollis). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Potato chip! — song of the American Goldfinch (Spinus tristis). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Here here. Come right here, dear. — song of the Baltimore Oriole (Icterus galbula). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Who cooks for you? — song of the Barred Owl (Strix varia). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Fire fire. Where where? Here here! See it, see it. — song of the Indigo Bunting (Passerina cyanea). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Clear. Wick, wick, wick. — song of the Northern Flicker (Colaptes auratus). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Quick, three beers! — song of the Olive-sided Flycatcher (Contopus cooperi). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Where are you? Here I am. — song of the Red-eyed Vireo (Vireo olivaceus). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Chubby chubby cheeks. Chubby cheeks. — song of the Ruby-crowned kinglet (Regulus calendula). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Here sweetie. — song of the Black-capped chickadee (Poecile atricapillus). (BUY ARTWORK)

Typographical posters of bird song
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
See me, pretty, pretty me. — song of the White-crowned sparrow (Zonotrichia leucophrys). (BUY ARTWORK)
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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