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 - Methods For Statistical Genetics And Genomics In Complex Human Disease

  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

• 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. After a brief review of methods for contingency tables, we will introduce the idea of generalized linear models, and focus on two special cases – multiple logistic regression and log-linear models. Each topic will be presented in detail by stating the model and covering parameter estimation and interpretation, inference, model building, regression diagnostics, and assessment of model fit. Finally, we will cover extensions to both models, including models for multinomial data, analysis of matched-pair data, and random effects models. Topics will be illustrated in class with examples, and we will discuss the use of Stata to conduct the analyses. Offered first half of fall term.

• GCB6990 - Lab Rotation

  Lab rotation

• 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

• What Joann’s Closing Says About the State of Retail, Knowledge @ Wharton - 03/18/2025
• Do Sin Taxes Actually Work? | Ben Lockwood, Knowledge @ Wharton - 03/18/2025
• Eliminating Taxes on Social Security Benefits: Who Gains and Who Loses?, Knowledge @ Wharton - 03/17/2025
• Why Google Dominates the Search Engine Market, Knowledge @ Wharton - 03/17/2025
• Lessons from COVID-19: Health Care’s Progress and Shortcomings Five Years Later, Knowledge @ Wharton - 03/12/2025
• How COVID-19 Changed the World, Knowledge @ Wharton - 03/11/2025
• How Tax Credits in Renewable Energy Finance Distort Outcomes, Knowledge @ Wharton - 03/11/2025
• Why Hybrid Intelligence Is the Future of Human-AI Collaboration, Knowledge @ Wharton - 03/11/2025
• The Psychology of Paying Taxes | Alex Rees-Jones, Knowledge @ Wharton - 03/11/2025
• How Tariffs Are Reshaping Aviation: Supply Chains, Costs, and Consumers, Knowledge @ Wharton - 03/07/2025