Art of the Personalized Oncogenomics Program
A poet is, after all, a sort of scientist, but engaged in a qualitative science in which nothing is measurable. He lives with data that cannot be numbered, and his experiments can be done only once. The information in a poem is, by definition, not reproducible. He becomes an equivalent of scientist, in the act of examining and sorting the things popping in [to his head], finding the marks of remote similarity, points of distant relationship, tiny irregularities that indicate that this one is really the same as that one over there only more important. Gauging the fit, he can meticulously place pieces of the universe together, in geometric configurations that are as beautiful and balanced as crystals.
— Lewis Thomas (The Medusa and the Snail: More Notes of a Biology Watcher)
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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.
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.
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.
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).
In additional to the posters, I've created remixes for your desktop at 4k resolution.
This 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.
Convolutional neural networks
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).
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
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.
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
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.
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.
Science Advances cover
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.
Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.
Browse my gallery of cover designs.
Regression modeling of time-to-event data with censoring
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).
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
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
The video recently premiered on YouTube.
Renders of the full scene are available as NFTs.