Distractions and amusements, with a sandwich and coffee.
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
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.
Clear, concise, legible and compelling.
Making a scientific graphical abstract? Refer to my practical design guidelines and redesign examples to improve organization, design and clarity of your graphical abstracts.
An in-depth look at my process of reacting to a bad figure — how I design a poster and tell data stories.
Building on the method I used to analyze the 2008, 2012 and 2016 U.S. Presidential and Vice Presidential debates, I explore word usagein the 2020 Debates between Donald Trump and Joe Biden.
We are celebrating the publication of our 50th column!
To all our coauthors — thank you and see you in the next column!
When modelling epidemics, some uncertainties matter more than others.
Public health policy is always hampered by uncertainty. During a novel outbreak, nearly everything will be uncertain: the mode of transmission, the duration and population variability of latency, infection and protective immunity and, critically, whether the outbreak will fade out or turn into a major epidemic.
The uncertainty may be structural (which model?), parametric (what is `R_0`?), and/or operational (how well do masks work?).
This month, we continue our exploration of epidemiological models and look at how uncertainty affects forecasts of disease dynamics and optimization of intervention strategies.
We show how the impact of the uncertainty on any choice in strategy can be expressed using the Expected Value of Perfect Information (EVPI), which is the potential improvement in outcomes that could be obtained if the uncertainty is resolved before making a decision on the intervention strategy. In other words, by how much could we potentially increase effectiveness of our choice (e.g. lowering total disease burden) if we knew which model best reflects reality?
This column has an interactive supplemental component (download code) that allows you to explore the impact of uncertainty in `R_0` and immunity duration on timing and size of epidemic waves and the total burden of the outbreak and calculate EVPI for various outbreak models and scenarios.
Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Uncertainty and the management of epidemics. Nature Methods 17.
Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Modeling infectious epidemics. Nature Methods 17:455–456.
Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: The SEIRS model for infectious disease dynamics. Nature Methods 17:557–558.
Our design on the cover of Nature Genetics's August 2020 issue is “Dichotomy of Chromatin in Color” . Thanks to Dr. Andy Mungall for suggesting this terrific title.
The cover design accompanies our report in the issue Gagliardi, A., Porter, V.L., Zong, Z. et al. (2020) Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes. Nature Genetics 52:800–810.