OP-11 Assessing Equivalent and Inverse Change in Genes between Diverse Experiments
Presenting Author: Lisa Neums, University of Kansas Medical Center
Abstract: It is important to identify when two exposures impact a molecular marker (e.g., a gene’s expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment versus control comparisons have not been developed. Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. We used a series of simulation studies to compare the two tests on the basis of balanced accuracy and F1-socre. We found that TOST is not efficient for identifying equivalently changed genes (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.96). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer of tumor tissue and peripheral blood mononuclear cells (PBMC) showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. In conclusion, a bootstrap test of the ECI is a promising new statistical approach 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 a specific treatment or exposure.
