Ensuring reliability in genomics data analysis is crucial. This presentation introduces two statistical methods—scDEED and mcRigor—that employ permutation-based techniques to improve reliability in single-cell data analyses, with a focus on data visualization and sparsity reduction. The core message is to use permutation as a flexible, assumption-lean technique to establish an appropriate numerical background/reference for detecting dubious findings in data analysis. 

 
2025 BDSIL Mentor, Jingyi Jessica Li, Professor of Statistics and Data Science (also affiliated with Biostatistics, Computational Medicine, and Human Genetics), leads a research group called the Junction of Statistics and Biology at UCLA. Dr. Li focuses on developing interpretable statistical methods for biomedical data, particularly single-cell and spatial transcriptomic data, with a strong emphasis on statistical rigor through the use of synthetic controls. A recipient of multiple awards, including the NSF CAREER Award, Sloan Research Fellowship, ISCB Overton Prize, and COPSS Emerging Leaders Award, her contributions are widely recognized in the fields of computational biology and statistics.