Irreproducible analysis

Journals and granting agencies are prodding scientists to make their data public. Once the data is public, other scientists can verify the conclusions. Or at least that’s how it’s supposed to work. In practice, it can be extremely difficult or impossible to reproduce someone else’s results. I’m not talking here about reproducing experiments, but simply reproducing the statistical analysis of experiments.

It’s understandable that many experiments are not practical to reproduce: the replicator needs the same resources as the original experimenter, and so expensive experiments are seldom reproduced. But in principle the analysis of an experiment’s data should be repeatable by anyone with a computer. And yet this is very often not possible.

Published analyses of complex data sets, such as microarray experiments, are seldom exactly reproducible. Authors inevitably leave out some detail of how they got their numbers. In a complex analysis, it’s difficult to remember everything that was done. And even if authors were meticulous to document every step of the analysis, journals do not want to publish such great detail. Often an article provides enough clues that a persistent statistician can approximately reproduce the conclusions. But sometimes the analysis is opaque or just plain wrong.

I attended a talk yesterday where Keith Baggerly explained the extraordinary steps he and his colleagues went through in an attempt to reproduce the results in a medical article published last year by Potti et al. He called this process “forensic bioinformatics,” attempting to reconstruct the process that lead to the published conclusions. He showed how he could reproduce parts of the results in the article in question by, among other things, reversing the labels on some of the groups. (For details, see “Microarrays: retracing steps” by Kevin Coombes, Jing Wang, and Keith Baggerly in Nature Medicine, November 2007, pp 1276-1277.)

While they were able to reverse-engineer many of the mistakes in the paper, some remain a mystery. In any case, they claim that the results of the paper are just wrong. They conclude “The idea … is exciting. Our analysis, however, suggests that it did not work here.”

The authors of the original article replied that there were a few errors but that these have been fixed and they didn’t effect the conclusions anyway. Baggerly and his colleagues disagree. So is this just a standoff with two sides pointing fingers at each other saying the other guys are wrong? No. There’s an important asymmetry between the two sides: the original analysis is opaque but the critical analysis is transparent. Baggerly and company have written code to carry out every tiny step of their analysis and made the Sweave code available for anyone to download. In other words, they didn’t just publish their paper, they published code to write their paper.

Sweave is a program that lets authors mix prose (LaTeX) with code (R) in a single file. Users do not directly paste numbers and graphs into a paper. Instead, they embed the code to produce the numbers and graphs, and Sweave replaces the code with the results of running the code. (Sweave embeds R inside LaTeX the way CGI embeds Perl inside HTML.) Sweave doesn’t guarantee reproducibility, but it is a first step.

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