Dealing with Confounders in Omics Analysis

Trends in Biotechnology
Wilson Wen Bin Goh, Limsoon Wong

Abstract

The Anna Karenina effect is a manifestation of the theory-practice gap that exists when theoretical statistics are applied on real-world data. In the course of analyzing biological data for differential features such as genes or proteins, it derives from the situation where the null hypothesis is rejected for extraneous reasons (or confounders), rather than because the alternative hypothesis is relevant to the disease phenotype. The mechanics of applying statistical tests therefore must address and resolve confounders. It is inadequate to simply rely on manipulating the P-value. We discuss three mechanistic elements (hypothesis statement construction, null distribution appropriateness, and test-statistic construction) and suggest how they can be designed to foil the Anna Karenina effect to select phenotypically relevant biological features.

Citations

Sep 25, 2020·Scientific Reports·Yaxing ZhaoWilson Wen Bin Goh
May 7, 2019·Journal of Bioinformatics and Computational Biology·Yaxing ZhaoWilson Wen Bin Goh
Sep 10, 2019·Drug Discovery Today·Wilson Wen Bin GohGuillaume Thibault
May 20, 2020·Genomics, Proteomics & Bioinformatics·Wilson Wen Bin Goh, Limsoon Wong

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