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

typography: exciting

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!

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.

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.

Potato chip! — song of the American Goldfinch (Spinus tristis). (BUY ARTWORK)
Here here. Come right here, dear. — song of the Baltimore Oriole (Icterus galbula). (BUY ARTWORK)
Who cooks for you? — song of the Barred Owl (Strix varia). (BUY ARTWORK)
Fire fire. Where where? Here here! See it, see it. — song of the Indigo Bunting (Passerina cyanea). (BUY ARTWORK)
Clear. Wick, wick, wick. — song of the Northern Flicker (Colaptes auratus). (BUY ARTWORK)
Quick, three beers! — song of the Olive-sided Flycatcher (Contopus cooperi). (BUY ARTWORK)
Where are you? Here I am. — song of the Red-eyed Vireo (Vireo olivaceus). (BUY ARTWORK)
Chubby chubby cheeks. Chubby cheeks. — song of the Ruby-crowned kinglet (Regulus calendula). (BUY ARTWORK)
Here sweetie. — song of the Black-capped chickadee (Poecile atricapillus). (BUY ARTWORK)
See me, pretty, pretty me. — song of the White-crowned sparrow (Zonotrichia leucophrys). (BUY ARTWORK)
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Classification and regression trees

Fri 28-07-2017
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.

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.

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.

Personal Oncogenomics Program 5 Year Anniversary Art

Wed 26-07-2017

The artwork was created in collaboration with my colleagues at the Genome Sciences Center to celebrate the 5 year anniversary of the Personalized Oncogenomics Program (POG).

5 Years of Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. (left) Cases ordered chronologically by case number. (right) Cases grouped by diagnosis (tissue type) and then by similarity within group.

The Personal Oncogenomics Program (POG) is a collaborative research study including many BC Cancer Agency oncologists, pathologists and other clinicians along with Canada's Michael Smith Genome Sciences Centre with support from BC Cancer Foundation.

The aim of the program is to sequence, analyze and compare the genome of each patient's cancer—the entire DNA and RNA inside tumor cells— in order to understand what is enabling it to identify less toxic and more effective treatment options.

Principal component analysis

Thu 06-07-2017
PCA helps you interpret your data, but it will not always find the important patterns.

Principal component analysis (PCA) simplifies the complexity in high-dimensional data by reducing its number of dimensions.

Nature Methods Points of Significance column: Principal component analysis. (read)

To retain trend and patterns in the reduced representation, PCA finds linear combinations of canonical dimensions that maximize the variance of the projection of the data.

PCA is helpful in visualizing high-dimensional data and scatter plots based on 2-dimensional PCA can reveal clusters.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Principal component analysis. Nature Methods 14:641–642.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.

$k$ index: a weightlighting and Crossfit performance measure

Wed 07-06-2017

Similar to the $h$ index in publishing, the $k$ index is a measure of fitness performance.

To achieve a $k$ index for a movement you must perform $k$ unbroken reps at $k$% 1RM.

The expected value for the $k$ index is probably somewhere in the range of $k = 26$ to $k=35$, with higher values progressively more difficult to achieve.

In my $k$ index introduction article I provide detailed explanation, rep scheme table and WOD example.

Dark Matter of the English Language—the unwords

Wed 07-06-2017

I've applied the char-rnn recurrent neural network to generate new words, names of drugs and countries.

The effect is intriguing and facetious—yes, those are real words.

But these are not: necronology, abobionalism, gabdologist, and nonerify.

These places only exist in the mind: Conchar and Pobacia, Hzuuland, New Kain, Rabibus and Megee Islands, Sentip and Sitina, Sinistan and Urzenia.

And these are the imaginary afflictions of the imagination: ictophobia, myconomascophobia, and talmatomania.

And these, of the body: ophalosis, icabulosis, mediatopathy and bellotalgia.

Want to name your baby? Or someone else's baby? Try Ginavietta Xilly Anganelel or Ferandulde Hommanloco Kictortick.

When taking new therapeutics, never mix salivac and labromine. And don't forget that abadarone is best taken on an empty stomach.

And nothing increases the chance of getting that grant funded than proposing the study of a new –ome! We really need someone to looking into the femome and manome.