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|
|Soft tissue ●||44||8.1|
|Head and neck ●||6||1.1|
|Central nervous system ●||5||0.9|
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).
One of my color tools, the
colorsnap application snaps colors in an image to a set of reference colors and reports their proportion.
Below is Times Square rendered using the colors of the MTA subway lines.
Drugs could be more effective if taken when the genetic proteins they target are most active.
Design tip: rediscover CMYK primaries.
Ruben et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine Science Translational Medicine 10 Issue 458, eaat8806.
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%).
Altman, N. & Krzywinski, M. (2018) Points of significance: Predicting with confidence and tolerance Nature Methods 15:843–844.
Krzywinski, M. & Altman, N. (2013) Points of significance: Importance of being uncertain. Nature Methods 10:809–810.
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.
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.
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.
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.
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.