Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology
By Keith A. Baggerly, Kevin R. Coombes. 2009
299 citations recorded by the source corpus.
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 incorporate 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.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/82857af265a46e88427635d3c9ccfdca3f7408b2
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In Molecular Biology
Citations in the corpus
- Reproducibility in Science: Improving the Standard for Basic and Preclinical Research (2015, unknown): “Similar observations have been made by others.(23,25) An analysis of 500 papers in the 50 top journals across scientific fields (those with highest impact factors) revealed only 9% deposited full primary data online.”
- Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach (2020, methodology): “Two statisticians later obtained the publicly available data and attempted to apply the algorithm [20].”
- Reuse of public genome-wide gene expression data (2012, result): “…case of failure is a recent clinical trial that was initiated on the basis of microarray studies of gene expression in cancer cell lines; the trial was stopped 51 only after independent researchers failed to reproduce the findings of the original studies 52 and found serious errors in the analysis.”