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Canada's Michael Smith Genome Sciences Centre (GSC) at BC Cancer is an international leader in genomics, proteomics and bioinformatics for precision medicine. By developing and deploying cutting-edge genome sequencing, computational and analytical technology, we are creating novel strategies to prevent and diagnose cancers and other diseases, uncovering new therapeutic targets and helping the world realize the social and economic benefits of genome science.
We are the Canadian node of the Earth Biogenome Project.

Art of the Personalized Oncogenomics Program

In research the horizon recedes as we advance, and is no nearer at sixty than it was at twenty. As the power of endurance weakens with age, the urgency of the pursuit grows more intense ... And research is always incomplete.
— Mark Pattison (Isaac Casaubon)

1 · What do the circles mean?

The legend can be printed at 4" × 6". The bitmap resolution is 600 dpi.


 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Quick legend. 5 Years of Personalized Oncogenomics Project at Canada's Michael Smith Genome Sciences Centre. The poster shows 545 cancer cases.

2 · A case for a visual case summary

For every case, we sequence the DNA to study the genome structure and the RNA to discover which genes are expressed and to what extent. The analysis is quite complex and brings together many steps: sequence alignment, structural variation detection, expression profiling, pathway analysis and so on. Every case is "summarized" by a lengthy report, such as the one below, which can run to over 40 pages.

Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Center / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A report for a typical POG case is about 40–50 pages.

One of the goals of the 5-year anniversary art was to represent the cases in a way to clearly show their number, classification as well as diversity. There are many metrics that can be used and I decided to choose the case's correlation to other cancer types.

correlation to TCGA cancer database

For every POG case, the gene expression of 1,744 key genes is compared to that of 1,000's of cases in the TCGA database of cancer samples. For a given cancer type in the TCGA database (e.g. BRCA), we visualize the correlations using box plots. The box plot is ideal for showing the distribution of values in a sample.

Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Center / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Every case is compared to a database of 1,000's of cases. Shown here are box plots for the Spearman correlation coefficient between the gene expression of the POG case and cancers of a specific type (e.g. BRCA, LUAD, etc).

The 10 largest Spearman correlation coefficients for the case shown above are

case    corr    type     tissue
-----------------------------------------------
POG661	0.436	BRCA	 Breast
POG661	0.371	PRAD	 Urologic
POG661	0.295	OV	 Gynecologic
POG661	0.257	UCEC	 Gynecologic
POG661	0.244	LUAD	 Thoracic
POG661	0.235	CESC_CAD Gynecologic
POG661	0.225	MB_Adult Central Nervous System
POG661	0.222	KICH	 Urologic
POG661	0.219	THCA	 Endocrine
POG661	0.208	UCS	 Gynecologic

In the figure below I show how the final encoding of the correlations is done. First, the top three correlations are taken—using more generates a busy look and diminishes visual impact. The correlations are encoded as concentric rings.

Because in most cases the differences in the top 3 correlations are relatively small, differences are emphasized by non-linearly scaling the encoding (the correlations are first scaled `r^3`).

Personalized Oncogenomics Program at Canada's Michael Smith Genome Sciences Center / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Case POG661. Median gene expression correlations with different cancer types from TCGA database. (A) Top 10 correlations shown as a bar plot. Color coding is by source tissue associated with the cancer type. (B) Top 10 correlations encoded as concentric rings. The width of the ring is proportional to the correlation. (C) Top 3 correlations. (D) Top 3 correlations scaled with a power to emphasize differences.

The type face is Proxima Nova. The colors for each tissue source are

         Gastrointestinal  234,62,144
                   Breast  237,75,51
                 Thoracic  242,130,56
              Gynecologic  253,188,61
              Soft tissue  244,217,59
                     Skin  193,216,51
                 Urologic  114,197,49
              Hematologic  29,166,68
            Head and neck  43,168,224
                Endocrine  71,82,178
   Central nervous system  127,65,146
                    Other  150,150,150
news + thoughts

Convolutional neural networks

Thu 17-08-2023

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

Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.

The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Convolutional neural networks. (read)

Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.

Background reading

Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

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

Neural network primer

Tue 10-01-2023

Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon

In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Neural network primer. (read)

Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.

We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Background reading

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

Cell Genomics cover

Mon 16-01-2023

Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.

Designed in collaboration with with Carlos Urzua.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)

Science Advances cover

Thu 05-01-2023

My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes.

More details about the design.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
My Science Advances cover that encodes sequence onto hypercubes (volume 9, issue 1, 6 January 2023). (more)

Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)

Regression modeling of time-to-event data with censoring

Thu 17-08-2023

If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber

In a follow-up to our Survival analysis — time-to-event data and censoring article, we look at how regression can be used to account for additional risk factors in survival analysis.

We explore accelerated failure time regression (AFTR) and the Cox Proportional Hazards model (Cox PH).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Regression modeling of time-to-event data with censoring. (read)

Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Regression modeling of time-to-event data with censoring. Nature Methods 19:1513–1515.

Music video for Max Cooper's Ascent

Tue 25-10-2022

My 5-dimensional animation sets the visual stage for Max Cooper's Ascent from the album Unspoken Words. I have previously collaborated with Max on telling a story about infinity for his Yearning for the Infinite album.

I provide a walkthrough the video, describe the animation system I created to generate the frames, and show you all the keyframes

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Frame 4897 from the music video of Max Cooper's Asent.

The video recently premiered on YouTube.

Renders of the full scene are available as NFTs.


© 1999–2023 Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA