Please join us for the Statistics Seminar scheduled from 11:30 a.m. to 1 p.m. on Friday, October 4.
Speaker: Professor Jingyi Jessica Li, Ph.D., University of California, Los Angeles
Title: Dissecting Double Dipping in Statistical Tests After Clustering
Abstract: Motivated by the widespread use of clustering followed by statistical testing in single-cell and spatial omics data analysis, this talk will address the issue of double dipping. We aim to explore whether double dipping is a significant concern and investigate how various data-splitting and data-simulation strategies can mitigate its impact on inflated false discovery rates (FDR). We will also discuss different perspectives on whether the inference should be conditional on the clustering step or not. In particular, we will highlight the influence of feature correlations on FDR inflation. Through simulation and real-data examples, we will demonstrate how our simulation-based strategy for correcting double dipping can lead to more reliable and insightful discoveries.
Meeting ID: 984 9349 0542
Passcode: 44417115
