The Junction of Statistics and Biology

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

Jessica received 2025 Guggenheim Fellowship

Jessica has been awarded a 2025 Guggenheim Fellowship by the John Simon Guggenheim Memorial Foundation. The fellowship is one of the most prestigious honors in the U.S., supporting individuals who have demonstrated exceptional scholarship or creative work across the sciences and arts. This year marks the 100th class of Guggenheim Fellows. Jessica was selected for her pioneering research at the […]

Catherine Higgins visited us from University College Dublin

Catherine was supported by the SFI-NSF student mobility programme to visit us for six weeks from early August to mid September. During the visit, she worked on a project on interrogating gene expression patterns in spatial trascriptomics data. It was a pleasure having Catherine with us this summer!

Jessica honored as ScholarGPS Highly Ranked Scholar

Jessica was recognized as a ScholarGPS Highly Ranked Scholar, placing her in the top 0.05% of scholars worldwide in the RNA-Seq field over the past five years. She would like to thank the tremendous work of the group members for this achievement.

Tianyi graduated and received UCLA Department of Statistics and Data Science’s Outstanding Ph.D. Student Award

Tianyi received his Ph.D. and the Outstanding Ph.D. Student Award at the 2024 Department of Statistics and Data Science Commencement. Congratulations!

Christy and Pan received the Warren Alpert Computational Biology and AI Network Fellowship

The Warren Alpert Computational Biology and AI Network Fellowship will support Christy Lee and Pan Liu to attend the Computational Genomics Summer Institute for two years and provide training opportunities. Congratulations to Christy and Pan!

Guan’ao received the Dissertation Year Fellowship

Congratulations to Guan’ao Yan for receiving the the UCLA Dissertation Year Fellowship!

Jessica’s work on generalized Pearson correlation squares was finally published in JASA

In this JASA paper, we generalized the squared Pearson correlation to capture a mixture of linear dependences between two real-valued variables, with or without an index variable that specifies the line memberships. When the index variable is not available, we developed a K-lines clustering algorithm. Thanks Heather for helping develop the gR2 R pakcage!

Christy’s scDEED paper was published in Nature Communications

In collabortion with Dr. Lucy Xia at HKUST, Christy co-developed scDEED for detecting dubious 2D embeddings by non-linear dimension reduction methods (such as t-SNE and UMAP) and optimizing the hyperparameters of these methods. The work was published in Nature Communications. Congratulations!

Guan’ao’s scReadSim paper was published in Nature Communications

Guan’ao developed scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that allows user-specified ground truths and generates synthetic sequencing reads (in a FASTQ or BAM file) by mimicking real data. The work was published in Nature Communications. Congratulations!

Xinzhou was a nominee of the 2023 UCLA Postdoctoral Scholars Award

Congratulations to Xinzhou! https://grad.ucla.edu/life-at-ucla/events/2023-postdoctoral-scholars-mentors-awards-ceremony/

SELECTED PUBLICATIONS

95. Liu, P. and Li, J.J. (2024). 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). C ategorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data. Nature Communications 16:1141.

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