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
Embrace me, surround me as the rush comes.Motorcycledrift deeper into the soundmore quotes

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

genomics + data mining

ICDM2012 Keynote

Needles in Stacks of Needles: genomics + data mining

Download talk

visual abstract

Martin Krzywinski @MKrzywinski
The talk introduces genomics and cancer biology to computer scientists and outlines areas in which data mining methods are being used to further our understanding of the genome. The theme is one of complexity and relevance — computers manage the former, but we are the ultimate judges of the latter. (download talk, ICDM2012)


In 2001, the first human genome sequence was published. Now, just over 10 years later, we capable of sequencing a genome in just a few days. Massive parallel sequencing projects now make it possible to study the cancers of thousands of individuals. New data mining approaches are required to robustly interrogate the data for causal relationships among the inherently noisy biology. How does one identify genetic changes that are specific and causal to a disease within the rich variation that is either natural or merely correlated? The problem is one of finding a needle in a stack of needles. I will provide a non-specialist introduction to data mining methods and challenges in genomics, with a focus on the role visualization plays in the exploration of the underlying data.


The title of the talk was drawn from the paper

Gregory M. Cooper & Jay Shendure Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data Nature Reviews Genetics 12, 628-640 (September 2011)

I will be posting a full list of references for the talk shortly.


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