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data visualization + art
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
Art is Science in Love
— E.F. Weisslitz
what do the circles mean?
The legend can be printed at 4" × 6". The bitmap resolution is 600 dpi.
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.
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.
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`).
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
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news + thoughts
Music for the Moon: Flunk's 'Down Here / Moon Above'
The Sanctuary Project is a Lunar vault of science and art. It includes two fully sequenced human genomes, sequenced and assembled by us at Canada's Michael Smith Genome Sciences Centre.
The first disc includes a song composed by Flunk for the (eventual) trip to the Moon.
But how do you send sound to space? I describe the inspiration, process and art behind the work.
Happy 2021 `\pi` Day—
A forest of digits
Celebrate `\pi` Day (March 14th) and finally see the digits through the forest.
This year is full of botanical whimsy. A Lindenmayer system forest – deterministic but always changing. Feel free to stop and pick the flowers from the ground.
And things can get crazy in the forest.
Check out art from previous years: 2013 `\pi` Day and 2014 `\pi` Day, 2015 `\pi` Day, 2016 `\pi` Day, 2017 `\pi` Day, 2018 `\pi` Day and 2019 `\pi` Day.
Testing for rare conditions
All that glitters is not gold. —W. Shakespeare
The sensitivity and specificity of a test do not necessarily correspond to its error rate. This becomes critically important when testing for a rare condition — a test with 99% sensitivity and specificity has an even chance of being wrong when the condition prevalence is 1%.
We discuss the positive predictive value (PPV) and how practices such as screen can increase it.
Altman, N. & Krzywinski, M. (2021) Points of significance: Testing for rare conditions. Nature Methods 18:224–225.
Standardization fallacy
We demand rigidly defined areas of doubt and uncertainty! —D. Adams
A popular notion about experiments is that it's good to keep variability in subjects low to limit the influence of confounding factors. This is called standardization.
Unfortunately, although standardization increases power, it can induce unrealistically low variability and lead to results that do not generalize to the population of interest. And, in fact, may be irreproducible.
Not paying attention to these details and thinking (or hoping) that standardization is always good is the "standardization fallacy". In this column, we look at how standardization can be balanced with heterogenization to avoid this thorny issue.
Voelkl, B., Würbel, H., Krzywinski, M. & Altman, N. (2021) Points of significance: Standardization fallacy. Nature Methods 18:5–6.
Graphical Abstract Design Guidelines
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.
"This data might give you a migrane"
An in-depth look at my process of reacting to a bad figure — how I design a poster and tell data stories.
