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I do not think you can start with anything precise. You have to achieve such precision as you can, as you go along. —Bertrand Russell

“I have no idea what’s awaiting me, or what will happen when this all ends. For the moment I know this: there are sick people and they need curing.” ―Albert Camus, The Plague

“Every day sadder and sadder news of its increase. In the City died this week 7496; and of them, 6102 of the plague. But it is feared that the true number of the dead this week is near 10,000 ....” —Samuel Pepys, 1665

“With one eye you are looking at the outside world, while with the other you are looking within yourself.” —Amedeo Modigliani

“Everything we see hides another thing, we always want to see what is hidden by what we see” — Rene Magritte

You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now. –Alan Watts

Robust regression generates more reliable estimates by detecting and downweighting outliers.

Simultaneous examination of multiple factors at two levels can reveal which have an effect.

International audience; Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will have a disease. Prediction makesit possible to identify best courses of action (e.g. treatment choice)without requiring understanding of the underlying mechanisms. In atypical research project, both inference and prediction are of value—we want to know how biological processes work and what will happennext. For example, we might want to infer which biological processesare associated with the dysregulation of a gene in a disease as well asclassify whether a subject has the disease and predict the besttherapy, such as drug intervention or invasive surgery.

Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

Machine learning extracts general principles from observed examples without explicit instructions.

Customize the experiment for the setting instead of adjusting the setting to fit a classical design.

The initially published paper contained an error in Table 1: in the rightmost column (y), “0.09” should have been “–0.09.” This error has been corrected in the PDF and HTML versions of the article.

Tabulating the number of objects in categories of interest dates back to the earliest records of commerce and population censuses.

A P value measures a sample's compatibility with a hypothesis, not the truth of the hypothesis.

The bootstrap can be used to assess uncertainty of sample estimates.

Motivation: In high-dimensional testing problems π0, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating π0 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems. Results: This article introduces a number of π0 estimators, the regression and ‘T’ methods that perform well with discrete test statistics and also assesses how well methods developed for or adapted from continuous tests perform with discrete tests. We demonstrate the usefulness of these estimators in the analysis of high-throughput biological RNA-seq and single-nucleotide polymorphism data. Availability and implementation: implemented in R Contact: nsa1@psu.edu or naomi@psu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

With four parameters I can fit an elephant and with five I can make him wiggle his trunk.—John von Neumann

It is important to understand both what a classification metric expresses and what it hides.

Nat. Methods 14, 757–758 (2017); published online 28 July 2017; corrected after print 28 July 2017 In the version of this article initially published, the expression (g1, g2) used to describe a sample subset in the Figure 1 legend was incorrect. The correct expression is (Ig(S1), Ig(S2)). The error has been corrected in the HTML and PDF versions of the article.

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

Correlation implies association, but not causation. Conversely, causation implies association, but not correlation.

Background Two-channel (or two-color) microarrays are cost-effective platforms for comparative analysis of gene expression. They are traditionally analysed in terms of the log-ratios (M-values) of the two channel intensities at each spot, but this analysis does not use all the information available in the separate channel observations. Mixed models have been proposed to analyse intensities from the two channels as separate observations, but such models can be complex to use and the gain in efficiency over the log-ratio analysis is difficult to quantify. Mixed models yield test statistics for the null distributions can be specified only approximately, and some approaches do not borrow strength between genes. Results This article reformulates the mixed model to clarify the relationship with the traditional log-ratio analysis, to facilitate information borrowing between genes, and to obtain an exact distributional theory for the resulting test statistics. The mixed model is transformed to operate on the M-values and A-values (average log-expression for each spot) instead of on the log-expression values. The log-ratio analysis is shown to ignore information contained in the A-values. The relative efficiency of the log-ratio analysis is shown to depend on the size of the intraspot correlation. A new separate channel analysis method is proposed that assumes a constant intra-spot correlation coefficient across all genes. This approach permits the mixed model to be transformed into an ordinary linear model, allowing the data analysis to use a well-understood empirical Bayes analysis pipeline for linear modeling of microarray data. This yields statistically powerful test statistics that have an exact distributional theory. The log-ratio, mixed model and common correlation methods are compared using three case studies. The results show that separate channel analyses that borrow strength between genes are more powerful than log-ratio analyses. The common correlation analysis is the most powerful of all. Conclusions The common correlation method proposed in this article for separate-channel analysis of two-channel microarray data is no more difficult to apply in practice than the traditional log-ratio analysis. It provides an intuitive and powerful means to conduct analyses and make comparisons that might otherwise not be possible.

Nat. Methods 13, 603–604 (2016); published online 28 July 2016; corrected after print 16 September 2016 In the version of this article initially published, the expression defining the Fβ score was incorrect. The correct expression is Fβ = (1 + β2) (Precision × Recall)/(β2× Precision + Recall).The error has been corrected in the HTML and PDF versions of the article.