Statistics Seminar

Abstract: Simulation is the Swiss Army knife of biomedical data science: by using historical data to help researchers imagine hypothetical experimental results, it can inform study design, method benchmarking, and statistical inference. For this reason, a flurry of research activity has centered around methodology for designing realistic data-driven simulators. Unfortunately, many of these simulators are treated as black boxes — their interfaces do not invite users to tinker with any underlying components or learn the statistical ideas behind them. To address this, we draw from the literature on interactive systems design and re-imagine the interface to the scDesign3 family of multi-omics simulators. We define new abstractions that make the simulation workflow more modular, composable, and accessible. We also introduce visualizations that encourage exploration and criticism of the learned models. Some case studies on microbiome and single-cell data highlight how this approach can simplify power analysis and synthetic null hypothesis testing. Bring a laptop... you may be asked to write code.