was named to the MIT Technology Review’s “Innovators Under 35” List.
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Dr. Li’s research is at the interface between statistics and biology. Her primary research interest lies in developing new statistical methods for understanding biological questions, especially those related to large-scale genomic and transcriptomic data. The specific topics she has examined include:
Bioinformatics / Statistical Genomics:
• Statistical methods for analyzing next-generation bulk and single-cell RNA sequencing data
• Using statistics to quantitate the Central Dogma, a fundamental principle in molecular biology
• Comparative genomics: developing novel statistical methods to investigate conserved or divergent biological phenomena in different tissue and cell types across multiple species
• Novel statistical methods for imputing missing data or extracting hidden information from various types of genomics data
• Identification of gene-gene co-expression and protein-DNA and protein-RNA interactions using diverse genomic data
• Measures of association
• Neyman-Pearson classification that controls the prioritized type of error in binary classification
• High-dimensional linear model inference and variable selection
• Community detection in a bipartite network with node covariates
• P-value free control of false discovery rates
• Labeling ambiguity issue in multi-class classification
Dr. Jingyi Jessica Li is an Associate Professor in the Department of Biostatistics, UCLA Fielding School of Public Health. Her specific appointments are in the Department of Statistics (primary) and the departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at University of California, Los Angeles (UCLA). She is also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics.
Prior to joining UCLA in 2013, Li obtained her Ph.D. degree from the Interdepartmental Group in Biostatistics at University of California, Berkeley, where she worked with Profs. Peter J. Bickel and Haiyan Huang. Li received her B.S. (summa cum laude) from the Department of Biological Sciences and Technology at Tsinghua University, China in 2007. Li and her students 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, her research interests include association measures, asymmetric classification, and high-dimensional variable selection. On the biomedical application side, her research interests include bulk and single-cell RNA sequencing, comparative genomics, and information flow in the central dogma.
Li is the recipient of the Hellman Fellowship (2015), the PhRMA Foundation Research Starter Grant in Informatics (2017), the Alfred P. Sloan Research Fellowship (2018), the Johnson & Johnson WiSTEM2D Math Scholar Award (2018), the NSF CAREER Award (2019), the UCLA DGSOM Keck W. M. Keck Foundation Junior Faculty Award (2020), and the MIT Technology Review 35 Innovators Under 35 China (2020).
Please check with the faculty member or their office about availability to serve during current academic period.
Li, J. J., & Biggin, M. D. (2015). Gene expression. Statistics requantitates the central dogma. Science (New York, N.Y.), 347(6226), 1066–1067.
Ye, Y., & Li, J. J. (2016). NMFP: a non-negative matrix factorization based preselection method to increase accuracy of identifying mRNA isoforms from RNA-seq data. BMC genomics, 17 Suppl 1(Suppl 1), 11.
Gao, R., & Li, J. J. (2017). Correspondence of D. melanogaster and C. elegans developmental stages revealed by alternative splicing characteristics of conserved exons. BMC genomics, 18(1), 234. https://doi.org/10.1186/s12864-017-3600-2
Li, W. V., & Li, J. J. (2018). Modeling and analysis of RNA-seq data: a review from a statistical perspective. Quantitative biology (Beijing, China), 6(3), 195–209.
Li, W. V., & Li, J. J. (2018). An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature communications, 9(1), 997.
Li, W. V., & Li, J. J. (2019). A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics (Oxford, England), 35(14), i41–i50.
Li, J. J., & Tong, X. (2020). Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines. Patterns (New York, N.Y.), 1(7), 100115.
Song, D., & Li, J. J. (2021). PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome biology, 22(1), 124.
Li J. J. (2021). A new bioinformatics tool to recover missing gene expression in single-cell RNA sequencing data. Journal of molecular cell biology, 13(1), 1–2.
Xi, N. M., & Li, J. J. (2021). Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data. Cell systems, 12(2), 176–194.e6.