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statistics + data

We celebrate 50 columns of good explanations of statistics.
Since 2013, our Nature Methods Points of Significance column has been offering crisp explanations and practical suggestions about best practices in statistical analysis and reporting. To all our coauthors — thank you and see you in the next column!

Nature Methods: Points of Significance

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Points of Significance column in Nature Methods. (Launch of Points of Significance)
50 | Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Uncertainty and the management of epidemics. Nature Methods 17 (https://doi.org/10.1038/s41592-020-0943-4).
49 | 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.
48 | Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Modeling infectious epidemics. Nature Methods 17:455–456.
47 | Grewal, J., Krzywinski, M. & Altman, N. (2020) Points of significance: Markov models — training and evaluation of hidden Markov models. Nature Methods 17:121–122.
46 | Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Hidden Markov models. Nature Methods 16:795–796.
45 | Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov chains. Nature Methods 16:663–664.
44 | Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nature Methods 16:451–452.
43 | Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.
42 | Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.
41 | Altman, N. & Krzywinski, M. (2018) Points of significance: Predicting with confidence and tolerance Nature Methods 15:843–844.
40 | Smucker, B., Krzywinski, M. & Altman, N. (2018) Points of significance: Optimal experimental design Nature Methods 15:559–560.
39 | Altman, N. & Krzywinski, M. (2018) Points of significance: Curse(s) of dimensionality Nature Methods 15:299–400.
38 | Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of significance: Statistics vs machine learning. Nature Methods 15:233–234.
37 | Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of significance: Machine learning: supervised methods. Nature Methods 15:5–6.
36 | Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of significance: Machine learning: a primer. Nature Methods 14:1119–1120.
35 | Altman, N. & Krzywinski, M. (2017) Points of significance: Ensemble methods: Bagging and random forests. Nature Methods 14:933–934.
34 | Krzywinski, M. & Altman, N. (2017) Points of significance: Classification and regression trees. Nature Methods 14:757–758.
33 | Lever, J., Krzywinski, M. & Altman, N. (2017) Points of significance: Principal component analysis. Nature Methods 14:641–642.
32 | Altman, N. & Krzywinski, M. (2017) Points of significance: Clustering. Nature Methods 14:545–546.
31 | Altman, N. & Krzywinski, M. (2017) Points of significance: Tabular data. Nature Methods 14:329–330.
30 | Altman, N. & Krzywinski, M. (2017) Points of significance: Interpreting P values. Nature Methods 14:213–214.
29 | Altman, N. & Krzywinski, M. (2017) Points of significance: P values and the search for significance. Nature Methods 14:3–4.
28 | Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Regularization. Nature Methods 13:803–804.
27 | Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Model selection and overfitting. Nature Methods 13:703–704.
26 | Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Classifier evaluation. Nature Methods 13:603–604.
25 | Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
24 | Altman, N. & Krzywinski, M. (2016) Points of significance: Regression diagnostics. Nature Methods 13:385–386.
23 | Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.
22 | Krzywinski, M. & Altman, N. (2015) Points of significance: Multiple linear regression. Nature Methods 12:1103–1104.
21 | Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nature Methods 12:999–1000.
20 | Altman, N. & Krzywinski, M. (2015) Points of significance: Association, correlation and causation. Nature Methods 12:899–900.
19 | Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of significance: Bayesian networks. Nature Methods 12:799–800.
18 | Kulesa, A., Krzywinski, M., Blainey, P. & Altman, N. (2015) Points of significance: Sampling distributions and the bootstrap. Nature Methods 12:477–478.
17 | Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of significance: Bayesian statistics. Nature Methods 12:277–278.
16 | Puga, J.L, Krzywinski, M. & Altman, N. (2015) Points of significance: Bayes' theorem. Nature Methods 12:277–278.
15 | Altman, N. & Krzywinski, M. (2015) Points of significance: Split plot design. Nature Methods 12:165–166.
14 | Altman, N. & Krzywinski, M. (2015) Points of significance: Sources of variation. Nature Methods 12:5–6.
13 | Krzywinski, M., Altman, N. (2014) Points of significance: Two factor designs. Nature Methods 11:1187–1188.
12 | Krzywinski, M., Altman, N. & Blainey, P. (2014) Points of significance: Nested designs. Nature Methods 11:977–978.
11 | Blainey, P., Krzywinski, M. & Altman, N. (2014) Points of significance: Replication. Nature Methods 11:879–880.
10 | Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699–700.
9 | Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments. Nature Methods 11:597–598.
8 | Krzywinski, M. & Altman, N. (2014) Points of significance: Non-parametric tests. Nature Methods 11:467–468.
7 | Krzywinski, M. & Altman, N. (2014) Points of significance: Comparing samples — Part II — Multiple testing. Nature Methods 11:355–356.
6 | Krzywinski, M. & Altman, N. (2014) Points of significance: Comparing samples — Part I — t–tests. Nature Methods 11:215–216.
5 | Krzywinski, M. & Altman, N. (2014) Points of significance: Visualizing samples with box plots. Nature Methods 11:119–120.
4 | Krzywinski, M. & Altman, N. (2013) Points of significance: Power and sample size. Nature Methods 10:1139–1140.
3 | Krzywinski, M. & Altman, N. (2013) Points of significance: Significance, P values and t–tests. Nature Methods 10:1041–1042.
2 | Krzywinski, M. & Altman, N. (2013) Points of significance: Error bars. Nature Methods 10:921–922.
1 | Krzywinski, M. & Altman, N. (2013) Points of significance: Importance of being uncertain. Nature Methods 10:809–810.

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news + thoughts

Points of Significance celebrates 50th column

Mon 24-08-2020

We are celebrating the publication of our 50th column!

To all our coauthors — thank you and see you in the next column!

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance: Celebrating 50 columns of clear explanations of statistics. (read)

Uncertainty and the management of epidemics

Mon 24-08-2020

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Uncertainty and the management of epidemics. (read)

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Uncertainty and the management of epidemics. (Interactive supplemental materials)

Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Uncertainty and the management of epidemics. Nature Methods 17.

Background reading

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.

Cover of Nature Genetics August 2020

Mon 03-08-2020

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Dichotomy of Chromatin in Color. Nature Genetics, August 2020 issue. (read more)

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.

Poster Design Guidelines

Wed 15-07-2020

Clear, concise, legible and compelling.

The PDF template is a poster about making posters. It provides design, typography and data visualiation tips with minimum fuss. Follow its advice until you have developed enough design sobriety and experience to know when to go your own way.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Poster Design Guidelines — Clear, concise, legible and compelling..

The SEIRS model for infectious disease dynamics

Thu 18-06-2020

Realistic models of epidemics account for latency, loss of immunity, births and deaths.

We continue with our discussion about epidemic models and show how births, deaths and loss of immunity can create epidemic waves—a periodic fluctuation in the fraction of population that is infected.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: The SEIRS model for infectious disease dynamics. (read)

This column has an interactive supplemental component (download code) that allows you to explore epidemic waves and introduces the idea of the phase plane, a compact way to understand the evolution of an epidemic over its entire course.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: The SEIRS model for infectious disease dynamics. (Interactive supplemental materials)

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.

Background reading

Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Modeling infectious epidemics. Nature Methods 17:455–456.

Gene Machines

Fri 05-06-2020

Shifting soundscapes, textures and rhythmic loops produced by laboratory machines.

In commemoration of the 20th anniversary of Canada's Michael Smith Genome Sciences Centre, Segue was commissioned to create an original composition based on audio recordings from the GSC's laboratory equipment, robots and computers—to make “music” from the noise they produce.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Gene Machines by Segue. Now available on vinyl.

Virus Mutations Reveal How COVID-19 Really Spread

Mon 01-06-2020

Genetic sequences of the coronavirus tell story of when the virus arrived in each country and where it came from.

Our graphic in Scientific American's Graphic Science section in the June 2020 issue shows a phylogenetic tree based on a snapshot of the data model from Nextstrain as of 31 March 2020.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Virus Mutations Reveal How COVID-19 Really Spread. Text by Mark Fischetti (Senior Editor), art direction by Jen Christiansen (Senior Graphics Editor), source: Nextstrain (enabled by data from GISAID).

Cover of Nature Cancer April 2020

Mon 27-04-2020

Our design on the cover of Nature Cancer's April 2020 issue shows mutation spectra of patients from the POG570 cohort of 570 individuals with advanced metastatic cancer.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Each ellipse system represents the mutation spectrum of an individual patient. Individual ellipses in the system correspond to the number of base changes in a given class and are layered by mutation count. Ellipse angle is controlled by the proportion of mutations in a class within the sample and its size is determined by a sigmoid mapping of mutation count scaled within the layer. The opacity of each system represents the duration since the diagnosis of advanced disease. (read more)

The cover design accompanies our report in the issue Pleasance, E., Titmuss, E., Williamson, L. et al. (2020) Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat Cancer 1:452–468.