Jingyi Jessica Li Research Group

University of California, Los Angeles

How can statistical models reveal which molecular processes drive cell-to-cell transcriptome variation across populations and within single cells? The research group develops interpretable statistical methods for biomedical data, building tools that quantify regulatory mechanisms across transcriptomes. The group extracts hidden information from transcriptomics data by designing algorithms and simulators to evaluate data-generation assumptions and benchmarking analysis workflows. Researchers construct synthetic negative controls and other statistical controls to ensure rigor and control false discoveries in high-throughput analyses. The group implements and releases software (for example, scDesign3) to simulate realistic single-cell and spatial omics data for method evaluation and experimental design.