Mingyao Li

• Diana Pauly, Divyansh Agarwal, Nicholas Dana, Nicole Schafer, Josef Biber, Kirsten A. Wunderlich, Yassin Jabri, Tobias Straub, Nancy Zhang, Avneesh K. Gautam, Bernhard H.F. Weber, Stefanie M. Hauck, Mijin Kim, Christine A. Curcio, Dwight Stambolian, Mingyao Li, Antje Grosch (2019), Cell-Type-Specific Complement Expression in the Healthy and Diseased Retina, Cell Reports, 29 (9), pp. 2835-2848.
• Xuran Wang, Jihwan Park, Katalin Susztak, Nancy Zhang, Mingyao Li (2019), Bulk tissue cell type deconvolution with multi-subject single-cell expression reference, Nature Communications, 10 (380).
• Jingshu Wang, Mo Huang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, John Murray, Arjun Raj, Mingyao Li, Nancy Zhang (2018), Gene Expression Distribution Deconvolution in Single Cell RNA Sequencing, Proceedings of the National Academy of Sciences of the United States of America, 115(28): E6437-E6446 ().
• Mo Huang, Jingshu Wang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, Roberto Bonasio, John Murray, Arjun Raj, Mingyao Li, Nancy Zhang (2018), SAVER: Gene expression recovery For UMI-based single cell RNA sequencing, Nature Methods, 15 (), pp. 539-542.
• Hanrui Zhang, Nancy Zhang, Mingyao Li, Muredach P. Reilly (2018), First Giant Steps Toward a Cell Atlas of Atherosclerosis, Circulation Research, 122 (), pp. 1632-1634.
• Cheng Jia, Yu Hu, Derek Kelly, Junhyong Kim, Mingyao Li, Nancy Zhang (2017), Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data, Nucleic Acids Research, 45 (19), pp. 10978-10988.
• Yuchao Jiang, Nancy Zhang, Mingyao Li (2017), SCALE: modeling allele-specific gene expression by single-cell RNA sequencing, Genome Biology, 18 (74).

Current Courses

• BSTA7870 - Stat Genetics/hum Dis

  This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. This course will cover statistical methods for the analysis of genetics and genomics data. Topics covered will include genetic linkage and association analysis, analysis of next-generation sequencing data, including those generated from DNA sequencing and RNA sequencing experiments. Students will be exposed to the latest statistical methodology and computer tools on genetic and genomic data analysis. They will also read and evaluate current statistical genetics and genomics literature. Prerequisite: If course requirements not met, permission of instructor required.

  BSTA7870001

Past Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• BIOL9999 - Independent Study

  Advanced laboratory reserach with a member of the Biology Graduate Group.

• BSTA6990 - Lab Rotation

  Student lab rotation.

• BSTA7870 - Stat Genetics/Hum Dis

  This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. This course will cover statistical methods for the analysis of genetics and genomics data. Topics covered will include genetic linkage and association analysis, analysis of next-generation sequencing data, including those generated from DNA sequencing and RNA sequencing experiments. Students will be exposed to the latest statistical methodology and computer tools on genetic and genomic data analysis. They will also read and evaluate current statistical genetics and genomics literature. Prerequisite: If course requirements not met, permission of instructor required.

• BSTA7980 - Advanced Topics in Biostats

  This course is designed for second-year PhD students in Biostatistics. The goal is to provide an in-depth exploration of special topics within the field of biostatistics. The course covers a range of advanced statistical methods and their applications in various biostatistical domains, including clinical trials, causal inference, survival analysis, genetics and genomics, neuroimaging, and health informatics. The course emphasizes the unique aspects of these topics and their significance in biomedical research and public health. Throughout the course, ten faculty members will deliver presentations, each focusing on a special topic.

• BSTA8990 - Pre-Dissertation Lab Rot

• BSTA9200 - Tutorial: Research

• BSTA9900 - Master's Thesis

  Students in the PhD and MS programs will develop and conduct advanced research under the supervision of a biostatistics faculty mentor before completing an MS thesis.

• BSTA9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

• EPID6220 - App Reg/Categorical Data

  This course will provide in-depth treatment of several topics in categorical data analysis. We will cover a range of regression models, including logistic regression, multinomial logistic regression, proportional odds model, conditional logistic regression, shrinkage methods in machine learning, classification methods in machine learning, latent class models, interrupted time series, difference in difference, random effects models and generalized estimating equations. Topics will be illustrated in class with examples. Stata will be used for the course.

• GCB6990 - Lab Rotation

  Lab rotation

• GCB7990 - Independent Study

  Independent study course

• GCB8990 - Pre-Dissertation Research

  Pre-dissertation lab research

• GCB9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

Knowledge @ Wharton

• How Investor Wealth Grows With Delaware’s Politically Balanced Judiciary, Knowledge @ Wharton - 02/03/2026
• Your Organization’s Unwritten Rules and How to Fix Them, Knowledge @ Wharton - 02/03/2026
• Can Classroom Cell Phone Bans Boost Grades?, Knowledge @ Wharton - 02/03/2026
• Psychology of Love and Relationships in the Digital Age | Pinar Yildirim, Knowledge @ Wharton - 02/03/2026
• What the Fed’s Pause Signals for Economy and Markets, Knowledge @ Wharton - 01/30/2026
• CMO of Liquid I.V., Stacey Andrade-Wells, Knowledge @ Wharton - 01/29/2026
• Prediction Markets and the Future of Sports Betting Analytics, Knowledge @ Wharton - 01/28/2026
• Why Interest Rates Can’t Fix Deeper Economic Problems, Knowledge @ Wharton - 01/28/2026
• Why Education Still Matters for Student Athletes, Knowledge @ Wharton - 01/28/2026
• Here’s the Best Way to Onboard a Manager, Knowledge @ Wharton - 01/28/2026