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Abstract:
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It is often important to identify when two exposures impact a gene’s expression in similar ways; for example to learn that a new drug has a similar effect to an existing drug, to validate results, or to find common factors among different diseases. Nowadays, an increasing number of differential gene expression studies are publicly available which can be used to answer those research objectives. While comparing the effect sizes of the differential expression between studies, it is unclear what should be accepted as an equivalent effect. Here, we propose two approaches for evaluating this question: a bootstrap test of the existing Equivalent Change Index (ECI) statistic and performing Two One-Sided t-Tests (TOST). Using a simulation study, we found that TOST is not powerful for identifying equivalently changed gene expression values (F1-score = 6.64E-05), while the ECI bootstrap test shows good performance (F1-score = 0.94). In conclusion, a bootstrap test of the ECI is a promising new statistical framework for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by an exposure.
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