Statistics Seminar | Elif Acar | Automated Statistical Methods for High-Throughput Phenotyping Experiments
Apr 1, 2025
3:30PM to 4:30PM
Date/Time
Date(s) - 01/04/2025
3:30 pm - 4:30 pm
Speaker: Dr. Elif Acar (University of Guelph)
Research areas: high-dimensional dependence modelling, statistical genetics, nonparametric estimation, survival analysis, meta-analysis and replicability.
Location: BSB 121
Title: Automated Statistical Methods for High-Throughput Phenotyping Experiments
Abstract: Many health applications produce ever-increasing quantities of biological data. As such applications often rely on automated pipelines for data analysis, an important statistical challenge is to evaluate and refine these pipelines as more and more data are acquired. This challenge is exemplified by the high-throughput phenotyping experiments conducted by the International Mouse Phenotyping Consortium (IMPC), where multiple phenotype measurements are obtained for a small set of gene-edited mice and a large set of controls acquired continually over time. In order to increase the power of detecting the gene effect, model selection is a fundamental component of the automated pipeline. However, the effect of post-selection inference in this setting is not well understood. Moreover, due to the size and complexity of the data, gene function is assessed by combining the results of univariate phenotype analyses. However, analyzing multiple phenotypes simultaneously at the individual level stands to greatly improve the power of detection. In this talk, I present ongoing work on evaluating and improving the IMPC statistical pipeline along these lines of inquiry.