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Promises and Pitfalls of High-Throughput Biological Assays

  • Greg Finak
  • Raphael Gottardo
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1415)

Abstract

This chapter discusses some of the pitfalls encountered when performing biomedical research involving high-throughput “omics” data and presents some strategies and guidelines that researchers should follow when undertaking such studies. We discuss common errors in experimental design and data analysis that lead to irreproducible and non-replicable research and provide some guidelines to avoid these common mistakes so that researchers may have confidence in study outcomes, even if the results are negative. We discuss the importance of ranking and prespecifying hypotheses, performing power analysis, careful experimental design, and preplanning of statistical analyses in order to avoid the “fishing expedition” data analysis strategy, which is doomed to fail. The impact of multiple testing on false-positive rates is discussed, particularly in the context of the analysis of high-throughput data, and methods to correct for it are presented, as well as approaches to detect and correct for experimental biases and batch effects, which often plague high-throughput assays. We highlight the importance of sharing data and analysis code to facilitate reproducibility and present tools and software that are appropriate for this purpose.

Key words

Batch effects Confounding Experimental design Multiple testing Statistical analysis plan Reproducibility Replicability 

Notes

Acknowledgments

This work was supported by a Bill and Melinda Gates Foundation grant, the Vaccine Immunology Statistical Center, and NIH grants U01 AI068635-01, U19 AI089986-01, and R01 EB00840-08.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Fred Hutchinson Cancer Research CenterSeattleUSA

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