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Where am I supposed to go? Where was I supposed to know?Violet Indianaget lost in questionsmore quotes

visualization: needed


In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017


data visualization + art

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The BC Cancer Agency’s Personalized Oncogenomics Program (POG) is a clinical research initiative applying genomic sequencing to the diagnosis and treatment of patients with incurable cancers.

Art of the Personalized Oncogenomics Program

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry.
— Richard Feynman

Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Center / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The design on the posters is being used for the Vancouver Ride to Conquer Cancer cycling jersey. (buy a jersey, tour info)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The POG art shows 545 cases studied over the course of 5 years and is freely available as posters for printing and images for your desktop and presentation slides in both bitmap and PDF formats.

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases grouped by diagnosis (tissue type) and then by similarity within group. (zoom)

cancer is the difference of differences

As individuals, we all have slightly different genomes. If you compare the genomes of two people, you will find about 3 million base pair differences, which is about 0.1% of the genome.

This variation exists not only within the population but potentially also, to a lesser extent, among our cells, which number around 40 trillion. That's roughly 10,000 cells for each base in your 3 billion base genome. And each has a role to play.

POG cases, by tissue type
n %
Gastrointestinal 141 25
 
Breast 138 25
 
Thoracic 57 10
 
Gynecologic 45 8.3
 
Soft tissue 44 8.1
 
Skin 11 2.0
 
Urologic 8 1.5
 
Hematologic 7 1.3
 
Head and neck 6 1.1
 
Endocrine 5 0.9
 
Central nervous system 5 0.9
 
Other 78 14
 
ALL 545

One consequence of this complexity and variation is that changes in the genome (through mutation or other processes) can have very different effects, depending on both the change and the genome. Cancer is a phenomena in which cells' ability to organize themselves as they divide is altered due to changes in the genome. It is an incredibly complex biological phenomenon—considering all the genomes in the population and all the possible changes that may arise, there is truly an inexhaustible number of ways in which the genome can break.

classifying cancer

Cancers are classified according to their site of origin, such as lung, breast, liver, or colon. This is a coarse grouping—within each group there are many subtypes with differences in response to treatment and overall behaviour.

diversities among clinical cases

The design of the POG art highlights the diversity and similarity among cases. The diversity is what makes the study of cancer difficult and the similarities are what makes inference possible.

Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Center / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca

Each case is represented by three concentric rings. The width of each ring represents the extent to which the case is similar (as measured by correlation) to cancers of the type encoded by the color of the ring (see Methods).

remixes

In additional to the posters, I've created remixes for your desktop at 4k resolution.


 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
png
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
png
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
png
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)

 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
png
5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases. Cases ordered chronologically by case number. (zoom)

Ride to Conquer Cancer — Data-powered, Human-driven

Ths year, the cyclists in the Ride to Conquer Cancer will not only have the chance to raise money for research (as they've always done) but also do so while wearing data (as they've never done before).

You can purchase your own data-powered and human-driven cycling jersey.

VIEW ALL

news + thoughts

Predicting with confidence and tolerance

Wed 07-11-2018
I abhor averages. I like the individual case. —J.D. Brandeis.

We focus on the important distinction between confidence intervals, typically used to express uncertainty of a sampling statistic such as the mean and, prediction and tolerance intervals, used to make statements about the next value to be drawn from the population.

Confidence intervals provide coverage of a single point—the population mean—with the assurance that the probability of non-coverage is some acceptable value (e.g. 0.05). On the other hand, prediction and tolerance intervals both give information about typical values from the population and the percentage of the population expected to be in the interval. For example, a tolerance interval can be configured to tell us what fraction of sampled values (e.g. 95%) will fall into an interval some fraction of the time (e.g. 95%).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Predicting with confidence and tolerance. (read)

Altman, N. & Krzywinski, M. (2018) Points of significance: Predicting with confidence and tolerance Nature Methods 15:843–844.

Background reading

Krzywinski, M. & Altman, N. (2013) Points of significance: Importance of being uncertain. Nature Methods 10:809–810.

4-day Circos course

Wed 31-10-2018

A 4-day introductory course on genome data parsing and visualization using Circos. Prepared for the Bioinformatics and Genome Analysis course in Institut Pasteur Tunis, Tunis, Tunisia.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Composite of the kinds of images you will learn to make in this course.

Oryza longistaminata genome cake

Mon 24-09-2018

Data visualization should be informative and, where possible, tasty.

Stefan Reuscher from Bioscience and Biotechnology Center at Nagoya University celebrates a publication with a Circos cake.

The cake shows an overview of a de-novo assembled genome of a wild rice species Oryza longistaminata.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Circos cake celebrating Reuscher et al. 2018 publication of the Oryza longistaminata genome.

Optimal experimental design

Tue 31-07-2018
Customize the experiment for the setting instead of adjusting the setting to fit a classical design.

The presence of constraints in experiments, such as sample size restrictions, awkward blocking or disallowed treatment combinations may make using classical designs very difficult or impossible.

Optimal design is a powerful, general purpose alternative for high quality, statistically grounded designs under nonstandard conditions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Optimal experimental design. (read)

We discuss two types of optimal designs (D-optimal and I-optimal) and show how it can be applied to a scenario with sample size and blocking constraints.

Smucker, B., Krzywinski, M. & Altman, N. (2018) Points of significance: Optimal experimental design Nature Methods 15:599–600.

Background reading

Krzywinski, M., Altman, N. (2014) Points of significance: Two factor designs. Nature Methods 11:1187–1188.

Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699–700.

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments. Nature Methods 11:597–598.

The Whole Earth Cataloguer

Mon 30-07-2018
All the living things.

An illustration of the Tree of Life, showing some of the key branches.

The tree is drawn as a DNA double helix, with bases colored to encode ribosomal RNA genes from various organisms on the tree.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The circle of life. (read, zoom)

All living things on earth descended from a single organism called LUCA (last universal common ancestor) and inherited LUCA’s genetic code for basic biological functions, such as translating DNA and creating proteins. Constant genetic mutations shuffled and altered this inheritance and added new genetic material—a process that created the diversity of life we see today. The “tree of life” organizes all organisms based on the extent of shuffling and alteration between them. The full tree has millions of branches and every living organism has its own place at one of the leaves in the tree. The simplified tree shown here depicts all three kingdoms of life: bacteria, archaebacteria and eukaryota. For some organisms a grey bar shows when they first appeared in the tree in millions of years (Ma). The double helix winding around the tree encodes highly conserved ribosomal RNA genes from various organisms.

Johnson, H.L. (2018) The Whole Earth Cataloguer, Sactown, Jun/Jul, p. 89

Why we can't give up this odd way of typing

Mon 30-07-2018
All fingers report to home row.

An article about keyboard layouts and the history and persistence of QWERTY.

My Carpalx keyboard optimization software is mentioned along with my World's Most Difficult Layout: TNWMLC. True typing hell.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
TNWMLC requires seriously flexible digits. It’s 87% more difficult than using a standard Qwerty keyboard, according to Martin Krzywinski, who created it (Credit: Ben Nelms). (read)

McDonald, T. (2018) Why we can't give up this odd way of typing, BBC, 25 May 2018.