Jingyi Jessica Li
| Jingyi Jessica Li | |
|---|---|
| Native name | 李婧翌 |
| Born | 1985 |
| 🎓 Alma mater | Tsinghua University (B.S.) University of California, Berkeley (Ph.D.) |
| 💼 Occupation | |
| Known for |
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| 🌐 Website | jsb |
Jingyi Jessica Li (Chinese:李婧翌) is a professor of Statistics, Biostatistics, Human genetics, Computational medicine, and Bioinformatics at the University of California, Los Angeles. Her research integrates statistical principles with biological data analysis, particularly in genomics and transcriptomics.
Li has won several awards, including the Overton Prize[1] from the International Society for Computational Biology and the Emerging Leader Award[2] from COPSS. In 2025, she was appointed to a Guggenheim Fellowship.[3]
Education and career
Li started her undergraduate education at Tsinghua University in 2003. She moved to the University of California, Berkeley for her Ph.D., and then started as a faculty member at the University of California, Los Angeles in 2013.[1] As of 2025 she is a full professor.[4]
From 2022 to 2023, she was a Radcliffe Fellow at the Harvard Radcliffe Institute for Advanced Study and a visiting professor in the Department of Statistics at Harvard University.[5]
Research
Her work relates to transcription and translational control of protein expression levels in the central dogma and statistical methods for RNA-seq data at the bulk and single-cell levels.
Her 2011 Nature study suggested that transcription, rather than translation, remains the dominant factor regulating protein abundance, primarily influencing differences in protein expression levels across genes.[6]
Her research group developed a suite of single-cell data simulators, including scDesign,[7] scDesign2 that captures gene-gene correlations,[8] scDesign3 for single-cell and spatial multi-omics data,[9] and scReadSim for single-cell RNA-seq and ATAC-seq read simulation.[10] Besides, her group developed scImpute,[11] an imputation tool for missing gene expression values.
Her contributions also extend to statistical and computational methodologies, including Clipper,[12] a p-value-free false discovery rate (FDR) control method; ITCA, a criterion for guiding the combination of ambiguous class labels in multiclass classification;[13] and Neyman-Pearson classification, a framework for prioritizing the control of misclassification errors in critical classes.[14][15]
Her recent efforts advocate for the importance of statistical rigor in genomics data analysis. In a recent study, she and co-authors raised a warning in using popular RNA-seq differential expression (DE) methods blindly without checking the underlying assumptions. For example, in population-scale human RNA-seq samples where the negative binomial assumption for each gene does not hold, popular methods relying on this assumption can lead to excessive false discoveries, while non-parametric tests such as the Wilcoxon rank-sum test gives more reliable results.[16] Moreover, she developed scDEED,[17] a statistical method leveraging permutation techniques to evaluate and optimize embeddings produced by t-SNE and UMAP. scDEED detects dubious embeddings that fail to preserve mid-range distances and refines t-SNE and UMAP hyperparameters.
References
- ↑ 1.0 1.1 Fogg, Christiana N.; Kovats, Diane E.; Vingron, Martin (30 June 2023). "2023 ISCB Overton Prize: Jingyi Jessica Li". Bioinformatics. 39 (Supplement 1): i5–i6. doi:10.1093/bioinformatics/btad307. PMC 10311287 Check
|pmc=value (help). Retrieved 2025-06-03. - ↑ "Meet the 2023 COPSS Emerging Leader Awardees". Institute of Mathematical Statistics. 31 March 2023. Retrieved 2025-06-03.
- ↑ "Announcing the 2025 Guggenheim Fellows — Guggenheim Fellowships: Supporting Artists, Scholars, & Scientists". Guggenheim Foundation. 15 April 2025. Retrieved 4 June 2025.
- ↑ "Jingyi Jessica Li – UCLA Graduate Programs in Bioscience". Bioscience.UCLA.edu. University of California, Los Angeles. Retrieved 2025-06-03.
- ↑ "Jingyi Jessica Li". Radcliffe Institute for Advanced Study at Harvard University. Retrieved 4 June 2025.
- ↑ Li, Jingyi Jessica; Biggin, Mark D. (2015). "Statistics requantitates the central dogma". Science. 347 (6226): 1066–1067. Bibcode:2015Sci...347.1066L. doi:10.1126/science.aaa8332. OSTI 1353301. PMID 25745146. Retrieved 2025-02-03.
- ↑ Li, Wei Vivian; Li, Jingyi Jessica (2019). "A statistical simulator scDesign for rational scRNA-seq experimental design". Bioinformatics. Oxford University Press. 35 (14): i41–i50. doi:10.1093/bioinformatics/btz390. PMC 7755417 Check
|pmc=value (help). PMID 33351929 Check|pmid=value (help). - ↑ Sun, Tianyi; Song, Dongyuan; Li, Wei Vivian; Li, Jingyi Jessica (2021). "scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured". Genome Biology. BioMed Central. 22 (1): 163. doi:10.1186/s13059-021-02367-2. PMC 8144190 Check
|pmc=value (help). PMID 34044808 Check|pmid=value (help). - ↑ Song, Dongyuan; Wang, Qingyang; Yan, Guanao; Liu, Tianyang; Sun, Tianyi; Li, Jingyi Jessica (2024). "scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics". Nature Biotechnology. Nature Publishing Group. 42 (2): 247–252. doi:10.1038/s41587-023-01772-1. PMC 11182337 Check
|pmc=value (help). PMID 37169966 Check|pmid=value (help). - ↑ Yan, Guanao; Song, Dongyuan; Li, Jingyi Jessica (November 18, 2023). "scReadSim: a single-cell RNA-seq and ATAC-seq read simulator". Nature Communications. 14 (1): 7482. Bibcode:2023NatCo..14.7482Y. doi:10.1038/s41467-023-43162-w. PMC 10657386 Check
|pmc=value (help). PMID 37980428 Check|pmid=value (help). - ↑ Li, Wei Vivian; Li, Jingyi Jessica (2018). "An accurate and robust imputation method scImpute for single-cell RNA-seq data". Nature Communications. 9 (1): 997. Bibcode:2018NatCo...9..997L. doi:10.1038/s41467-018-03405-7. PMC 5843666. PMID 29520097.,
- ↑ Ge, Xinzhou; Chen, Yiling Elaine; Song, Dongyuan; McDermott, MeiLu; Woyshner, Kyla; Manousopoulou, Antigoni; Wang, Ning; Li, Wei; Wang, Leo D.; Li, Jingyi Jessica (2021). "Clipper: p-value-free FDR control on high-throughput data from two conditions". Genome Biology. 22 (1): 288. doi:10.1186/s13059-021-02506-9. PMC 8504070 Check
|pmc=value (help). PMID 34635147 Check|pmid=value (help). - ↑ Zhang, Qi; Zhang, Yu; Li, Jingyi Jessica (2023). "itca: an information-theoretic criterion for label aggregation in multi-class classification". Bioinformatics. 40 (1): 1246–1249. doi:10.1093/bioinformatics/btad770. PMC 10749738 Check
|pmc=value (help). PMID 37930802 Check|pmid=value (help). - ↑ Tong, Xin; Feng, Yang; Li, Jingyi Jessica (2018). "Neyman-Pearson classification algorithms and NP receiver operating characteristics". Science Advances. American Association for the Advancement of Science. 4 (2): eaao1659. arXiv:1608.03109. Bibcode:2018SciA....4.1659T. doi:10.1126/sciadv.aao1659. PMC 5804623. PMID 29423442.
- ↑ Zhang, Mingwei; Li, Jingyi Jessica (2023). "Hierarchical Neyman–Pearson classification for high-stakes decision making". Journal of the American Statistical Association. doi:10.1080/01621459.2023.2270657. Retrieved 2025-02-03.
- ↑ Li, Yumei; Ge, Xinzhou; Peng, Fanglue; Li, Wei; Li, Jingyi Jessica (2022). "Exaggerated false positives by popular differential expression methods when analyzing human population samples". Genome Biology. 23 (1): 216. doi:10.1186/s13059-022-02648-4. PMC 8922736 Check
|pmc=value (help). PMID 35292087 Check|pmid=value (help). - ↑ Xia, L.; Lee, C.; Li, J. J. (2024). "Statistical method scDEED for detecting dubious 2D single-cell embeddings". Nature Communications. 15 (1): 1753. doi:10.1038/s41467-024-45891-y. PMC 10897166 Check
|pmc=value (help). PMID 38409103 Check|pmid=value (help).
External links
- Jingyi Jessica Li publications indexed by Google Scholar
- Genomic processes described using biology and statistics - public talk by Li on the ABC Radio National Science Show
- Arriving at the junction of statistics and biology: my journey – Harvard Radcliffe Institute Helen Putnam Fellow Talk given by Li
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