Draft:Jingyi Jessica Li

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Li was born in Chongqing, China, and developed an early curiosity for science, history, and music. In high school, she discovered her passion for interdisciplinary research and resolved to pursue a career in academia. She completed her undergraduate studies at Tsinghua University, earning a B.S. in biological sciences with a minor in English. During this time, as microarray technologies flourished, she recognized the growing need for mathematics in biology and the importance of computation and quantitative thinking as high-throughput data became increasingly available. To bridge this gap, she took additional mathematics courses and decided to pursue graduate studies with a stronger focus on quantitative training.

She obtained her Ph.D. in Biostatistics from the University of California, Berkeley, in 2013, with a designated emphasis in computational biology. Her dissertation, supervised by Peter J. Bickel and Haiyan Huang, focused on developing statistical methods for analyzing high-throughput biological data.

Li joined UCLA as an assistant professor in 2013, was promoted to associate professor in 2019, and became a full professor in 2022. She holds joint faculty appointments in:

• Department of Statistics and Data Science
• Department of Biostatistics
• Department of Computational Medicine
• Department of Human Genetics
• Interdepartmental Ph.D. Program in Bioinformatics
• Institute for Quantitative and Computational Biosciences (QCBio)
• Jonsson Comprehensive Cancer Center (Gene Regulation Program Area)

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.

Her work has advanced the understanding of transcription and translational control of protein expression levels in the central dogma, contributed to the development of statistical methods for RNA-seq data at the bulk and single-cell levels, and advocated for the importance of statistical rigor in bioinformatics.

A critical contribution came from her reanalysis of a 2011 Nature study, where she demonstrated that transcription, rather than translation, remains the dominant factor regulating protein abundance, primarily influencing differences in protein expression levels across genes.{{cite journal |last1=Li |first1=Jingyi Jessica |last2=Biggin |first2=Mark D. |title=Statistics requantitates the central dogma |journal=Science |year=2015 |volume=347 |issue=6226 |pages=1066–1067 |doi=10.1126/science.aaa8332 |pmid=25745146 |bibcode=2015Sci...347.1066L |url=https://www.science.org/doi/full/10.1126/science.aaa8332 |access-date=2025-02-03}} This pivotal finding, published in Science, has been widely recognized and featured in the undergraduate textbook Molecular Cell Biology (8th Edition).

Her research group developed a suite of single-cell data simulators, including scDesign,{{cite journal

| last1 = Li

| first1 = Wei Vivian

| last2 = Li

| first2 = Jingyi Jessica

| title = A statistical simulator scDesign for rational scRNA-seq experimental design

| journal = Bioinformatics

| volume = 35

| issue = 14

| pages = i41–i50

| year = 2019

| publisher = Oxford University Press

| doi = 10.1093/bioinformatics/btz390

| pmid = 33351929

| pmc = 7755417

| url = https://academic.oup.com/bioinformatics/article/35/14/i41/5529133

}} scDesign2 that captures gene-gene correlations,{{cite journal

| last1 = Sun

| first1 = Tianyi

| last2 = Song

| first2 = Dongyuan

| last3 = Li

| first3 = Wei Vivian

| last4 = Li

| first4 = Jingyi Jessica

| title = scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

| journal = Genome Biology

| volume = 22

| issue = 1

| pages = 163

| year = 2021

| publisher = BioMed Central

| doi = 10.1186/s13059-021-02367-2

| doi-access = free

| pmid = 34044808

| pmc = 8144190

}} scDesign3 for single-cell and spatial multi-omics data, {{cite journal

| last1 = Song

| first1 = Dongyuan

| last2 = Wang

| first2 = Qingyang

| last3 = Yan

| first3 = Guanao

| last4 = Liu

| first4 = Tianyang

| last5 = Sun

| first5 = Tianyi

| last6 = Li

| first6 = Jingyi Jessica

| title = scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

| journal = Nature Biotechnology

| volume = 42

| issue = 2

| pages = 247–252

| year = 2024

| publisher = Nature Publishing Group

| doi = 10.1038/s41587-023-01772-1

| pmid = 37169966

| pmc = 11182337

}} and scReadSim for single-cell RNA-seq and ATAC-seq read simulation.{{cite journal

| last1 = Yan

| first1 = Guanao

| last2 = Song

| first2 = Dongyuan

| last3 = Li

| first3 = Jingyi Jessica

| title = scReadSim: a single-cell RNA-seq and ATAC-seq read simulator

| journal = Nature Communications

| volume = 14

| issue = 1

| pages = 7482

| date = November 18, 2023

| doi = 10.1038/s41467-023-43162-w

| pmid = 37980428

| pmc = 10657386

| bibcode = 2023NatCo..14.7482Y

}}

Besides, her group developed scImpute,{{cite journal |last1=Li |first1=Wei Vivian |last2=Li |first2=Jingyi Jessica |title=An accurate and robust imputation method scImpute for single-cell RNA-seq data |journal=Nature Communications |date=2018 |volume=9 |issue=1 |pages=997 |doi=10.1038/s41467-018-03405-7 |pmid=29520097 |pmc=5843666 |bibcode=2018NatCo...9..997L }}, an imputation tool for missing gene expression values.

Her contributions also extend to statistical and computational methodologies, including Clipper,{{cite journal |last1=Ge |first1=Xinzhou |last2=Chen |first2=Yiling Elaine |last3=Song |first3=Dongyuan |last4=McDermott |first4=MeiLu |last5=Woyshner |first5=Kyla |last6=Manousopoulou |first6=Antigoni |last7=Wang |first7=Ning |last8=Li |first8=Wei |last9=Wang |first9=Leo D. |last10=Li |first10=Jingyi Jessica |title=Clipper: p-value-free FDR control on high-throughput data from two conditions |journal=Genome Biology |date=2021 |volume=22 |issue=1 |pages=288 |doi=10.1186/s13059-021-02506-9 |doi-access=free |pmid=34635147 |pmc=8504070 }}

a p-value-free false discovery rate (FDR) control method; gR2, which generalizes the Pearson correlation squares to capture complex linear dependencies in bivariate data;{{cite arXiv

| eprint = 1811.09965

| title = Generalized Pearson correlation squares for capturing mixtures of bivariate linear dependences

| last1 = Li

| first1 = Jingyi Jessica

| last2 = Tong

| first2 = Xin

| last3 = Bickel

| first3 = Peter J.

| year = 2018

| class = stat.ME

}} ITCA, a criterion for guiding the combination of ambiguous class labels in multiclass classification;{{cite journal

| last1 = Zhang

| first1 = Qi

| last2 = Zhang

| first2 = Yu

| last3 = Li

| first3 = Jingyi Jessica

| title = itca: an information-theoretic criterion for label aggregation in multi-class classification

| journal = Bioinformatics

| volume = 40

| issue = 1

| pages = 1246–1249

| date = 2023

| doi = 10.1093/bioinformatics/btad770

| pmid = 37930802

| url = https://pubmed.ncbi.nlm.nih.gov/37930802/

}}

and Neyman-Pearson classification, a framework for prioritizing the control of misclassification errors in critical classes.{{cite journal

| last1 = Tong

| first1 = Xin

| last2 = Feng

| first2 = Yang

| last3 = Li

| first3 = Jingyi Jessica

| title = Neyman-Pearson classification algorithms and NP receiver operating characteristics

| journal = Science Advances

| volume = 4

| issue = 2

| pages = eaao1659

| year = 2018

| publisher = American Association for the Advancement of Science

| doi = 10.1126/sciadv.aao1659

| pmid = 29423442

| pmc = 5804623

| arxiv = 1608.03109

| bibcode = 2018SciA....4.1659T

}}{{cite journal |last1=Zhang |first1=Mingwei |last2=Li |first2=Jingyi Jessica |title=Hierarchical Neyman–Pearson classification for high-stakes decision making |journal=Journal of the American Statistical Association |date=2023 |volume= |issue= |pages= |doi=10.1080/01621459.2023.2270657 |url=https://www.tandfonline.com/doi/full/10.1080/01621459.2023.2270657 |access-date=2025-02-03}}

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.{{cite journal |last1=Li |first1=Yumei |last2=Ge |first2=Xinzhou |last3=Peng |first3=Fanglue |last4=Li |first4=Wei |last5=Li |first5=Jingyi Jessica |title=Exaggerated false positives by popular differential expression methods when analyzing human population samples |journal=Genome Biology |date=2022 |volume=23 |issue=1 |pages=216 |doi=10.1186/s13059-022-02648-4 |doi-access=free |pmid=35292087 |pmc=8922736 }} Moreover, she developed scDEED,{{cite journal |title=Statistical method scDEED for detecting dubious 2D single-cell embeddings |journal=Nature Communications |date=2024 |doi=10.1038/s41467-024-45891-y |pmid=38409103 |last1=Xia |first1=L. |last2=Lee |first2=C. |last3=Li |first3=J. J. |volume=15 |issue=1 |page=1753 |pmc=10897166 }} 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. She also proposed leveraging semi-synthetic negative control data to detect and eliminate false discoveries resulting from analysis biases, such as double dipping. An example is her method, ClusterDE,{{cite journal |title=Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses |journal=bioRxiv |date=2023-07-21 |doi=10.1101/2023.07.21.550107 |url=https://www.biorxiv.org/content/10.1101/2023.07.21.550107v1.full.pdf |last1=Song |first1=Dongyuan |last2=Chen |first2=Siqi |last3=Lee |first3=Christy |last4=Li |first4=Kexin |last5=Ge |first5=Xinzhou |last6=Li |first6=Jingyi Jessica |pmid=37546812 |pmc=10401959 }} a statistical approach designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains in single-cell and spatial transcriptomic data analysis while ensuring false discovery rate control regardless of clustering quality.

Li has received numerous awards for her contributions to statistics and computational biology, including:

• Johnson & Johnson Women in STEM2D Math Scholar Award (2018).{{cite web |title=2018 WiSTEM²D Scholars Award Winners Announced |url=https://www.jnj.com/innovation/2018-wistem2d-scholars-award-winners-announced |website=Johnson & Johnson |date=2018 |access-date=2025-02-03}}
• Sloan Research Fellowship (2018) – Recognizing early-career researchers in science and engineering.{{cite web |title=Alfred P. Sloan Foundation Research Fellowship – Jingyi Jessica Li |url=https://sloan.org/grant-detail/8617 |website=Alfred P. Sloan Foundation |access-date=2025-02-03}}
• NSF CAREER Award (2019) – For advancing bioinformatics methodology for single-cell RNA sequencing.{{cite web |title=NSF CAREER Award – Jingyi Jessica Li |url=https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846216 |website=National Science Foundation (NSF) |access-date=2025-02-03}}
• MIT Technology Review 35 Innovators Under 35 China (2020).{{cite web |title=Innovators Under 35 China (2020) |url=https://www.innovatorsunder35.com/the-list/2020/china/ |website=Innovators Under 35 |access-date=2025-02-03}}
• Overton Prize (2023) – Awarded by the International Society for Computational Biology (ISCB).{{cite web |title=UCLA Professor Jingyi Jessica Li Receives Overton Prize |url=https://www.iscb.org/ismbeccb2023-programme/distinguished-keynotes/jingyi-jessica-li |website=International Society for Computational Biology |date=May 30, 2023 |access-date=2025-02-03}}
• Emerging Leader Award (2023) – From the Committee of Presidents of Statistical Societies (COPSS).{{cite web |title=COPSS Leadership Academy |url=https://community.amstat.org/copss/awards/leadership-academy |website=Committee of Presidents of Statistical Societies (COPSS) |access-date=2025-02-03}}

• Genomic processes described using biology and statistics {{cite web |title=Genomic processes described using biology and statistics |url=https://www.abc.net.au/listen/programs/scienceshow/statistics-and-biology-come-together-to-describe-genomic-proces/12273710 |website=ABC Radio National |date=June 6, 2020 |access-date=2025-02-04}} – ABC Radio National Science Show
• Arriving at the junction of statistics and biology: my journey {{cite web |title=Arriving at the Junction of Statistics and Biology: My Journey |url=https://www.radcliffe.harvard.edu/event/2023-jingyi-jessica-li-fellow-presentation-virtual |website=Harvard Radcliffe Institute |date=2023 |access-date=2025-02-04}} – Harvard Radcliff Institute Helen Putnam Fellow Talk

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