Martin Krzywinski / Genome Sciences Center / Martin Krzywinski / Genome Sciences Center / - contact me Martin Krzywinski / Genome Sciences Center / on Twitter Martin Krzywinski / Genome Sciences Center / - Lumondo Photography Martin Krzywinski / Genome Sciences Center / - Pi Art Martin Krzywinski / Genome Sciences Center / - Hilbertonians - Creatures on the Hilbert Curve
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.

data visualization + art

Martin Krzywinski @MKrzywinski
To view the art you'll need a pair of red-blue 3D glasses.
The data will stand out—and you will too.

BD Genomics stereoscopic art exhibit — AGBT 2017

Art is science in love.
— E.F. Weisslitz

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Our art exhibit at AGBT 2017 asked new school questions in old school ways.

data in new dimensions

convergence of art, genomics and bioinformatics

In genomics, insights can hinge on a difference of one. One cellular mutation to go from healthy to diseased. One cell migration from tumor to metastasis. Even subtle differences in gene expression between healthy cells shapes their form and function.

In Data in New Dimensions, we’ve created an immersive data art experience celebrating the individuality and often underestimated influence of the single cell—captured by high-throughput single cell analysis.

Using the rich data from the very tools and instruments in this room, we’ve transformed data points back into cells and, informed by their differences, allowed those cells to once again rejoin the world of the viewer in the third dimension.

How do these canvases make you think about the difference of one in your work?

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Data in New Dimensions. BD Genomics art exhibit at AGBT 2017.

difference of one expression

This piece contrasts two different blood cell states, diseased versus healthy, in such a way that the differences manifest as depth. Cells on the base plane (the closest to the wall) represent healthy control cells, while diseased cells ascend increasingly closer to the viewer based on how different they are from their healthy counterpart.

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Blood cells: diseased versus healthy control.
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski

difference of one migration

This piece paints a picture of the diversity of disease, showing how the cells of a tumor and its metastasis vary in expression patterns. These differences are manifested in the piece through each cell’s position in the third dimension. Cells from the primary tumor exist on the base layer (closest to the wall). Cells from the metastatic site project into the room based on the degree of difference from the nearest primary tumor cell in their cluster.

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Primary tumor versus metastasis.
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski

difference of one function

This piece explores the expression differences that help determine a healthy cell’s role within an organism. Each cluster corresponds to a different cell type along the renal tubule, with that cluster’s depth mapping to its position along the tubule. Blood enters the tubule through the cells on the base layer (closest to the wall) and is filtered by the cells in the successively ascending layers. The remaining waste exits past the cells in the layer nearest to the viewer.

BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski
Mouse kidney.
BD Genomics 3D art exhibit - AGBT 2017 / Martin Krzywinski @MKrzywinski

news + thoughts

`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.

Dark Matter of the Genome—the nullomers

Wed 31-05-2017

An exploration of things that are missing in the human genome. The nullomers.

Julia Herold, Stefan Kurtz and Robert Giegerich. Efficient computation of absent words in genomic sequences. BMC Bioinformatics (2008) 9:167


Wed 31-05-2017
Clustering finds patterns in data—whether they are there or not.

We've already seen how data can be grouped into classes in our series on classifiers. In this column, we look at how data can be grouped by similarity in an unsupervised way.

Martin Krzywinski @MKrzywinski
Nature Methods Points of Significance column: Clustering. (read)

We look at two common clustering approaches: `k`-means and hierarchical clustering. All clustering methods share the same approach: they first calculate similarity and then use it to group objects into clusters. The details of the methods, and outputs, vary widely.

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

Background reading

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

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

...more about the Points of Significance column

What's wrong with pie charts?

Thu 25-05-2017

In this redesign of a pie chart figure from a Nature Medicine article [1], I look at how to organize and present a large number of categories.

I first discuss some of the benefits of a pie chart—there are few and specific—and its shortcomings—there are few but fundamental.

I then walk through the redesign process by showing how the tumor categories can be shown more clearly if they are first aggregated into a small number groups.

(bottom left) Figure 2b from Zehir et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. (2017) Nature Medicine doi:10.1038/nm.4333

Tabular Data

Tue 11-04-2017
Tabulating the number of objects in categories of interest dates back to the earliest records of commerce and population censuses.

After 30 columns, this is our first one without a single figure. Sometimes a table is all you need.

In this column, we discuss nominal categorical data, in which data points are assigned to categories in which there is no implied order. We introduce one-way and two-way tables and the `\chi^2` and Fisher's exact tests.

Altman, N. & Krzywinski, M. (2017) Points of Significance: Tabular data. Nature Methods 14:329–330.

...more about the Points of Significance column