The JSB lab is led by the PI, Dr. Jingyi Jessica Li, with around ten highly motivated graduate and undergraduate students. Our research is at the junction of statistics and biology, as our lab name JSB represents. We focus on developing statistical and computational methods motivated by important questions in biomedical sciences and abundant information in big genomic and health related data. On the statistical methodology side, our example interests include association measures, high-dimensional variable selection, and classification metrics. On the biomedical application side, our example interests include next-generation RNA sequencing, comparative genomics, and information flow in the central dogma.
If you are interested in our research, please check out our YouTube channel, Twitter, and Medium.
RECENT NEWS
SELECTED PUBLICATIONS
98. Liu, P. and Li, J.J. (2025). mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis. Nature Communications accepted. [ RECOMB 2025 ] [ SOFTWARE ]
91. Song, D., Chen, S., Lee, C., Li, K., Ge, X., and Li, J.J. (2025). Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses. Lecture Notes in Computer Science 15647:400-404; Sankararaman, S., ed.; Springer, Cham. (RECOMB 2025; proceeding)
85. Yan, G., Hua, S.H., and Li, J.J. (2025). Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data. Nature Communications 16:1141.