Keith A. Baggerly and Kevin R. Coombes, "Deriving Chemosensitivity from Cell Lines: the Need for Reproducible Research in High-Throughput Biology," to appear in the Annals of Applied Statistics.
Full Paper:
http://www.e-publications.org/ims/submi ... m=cfad51b7
or
http://www.ndns.nl/static/files/sls/pre ... dStats.pdf
Code/Data:
http://bioinformatics.mdanderson.org/Su ... roRsch-All
BibTeX:
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Copyright Notice:
Copyright holders include the journal/conference publisher.
Abstract:
High-throughput biological assays such as microarrays let us ask very detailed questions about how diseases operate, and promise to let us personalize therapy. Data processing, however, is often not described well enough to allow for exact reproduction of the results, leading to exercises in “forensic bioinformatics” where aspects of raw data and reported results are used to infer what methods must have been employed. Unfortunately, poor documentation can shift from an inconvenience to an active danger when it obscures not just methods but errors. In this report, we examine several related papers purporting to use microarray-based signatures of drug sensitivity derived from cell lines to predict patient response. Patients in clinical trials are currently being allocated to treatment arms on the basis of these results. However, we show in five case studies that the results incor- porate several simple errors that may be putting patients at risk. One theme that emerges is that the most common errors are simple (e.g., row or column offsets); conversely, it is our experience that the most simple errors are common. We then discuss steps we are taking to avoid such errors in our own investigations.
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