CCB Seminar Series

Speaker: Rong Ma, HSPH Professor

When: Monday, May 13, 3:00 PM ET

Topic: Principled and interpretable alignability testing and integration of single-cell data

Hybrid: Countway L1-032, Click here

• Dr. Ma will be staying after for those interested in a more in depth, post-seminar discussion of topics reviewed.

Abstract: Aligning and integrating different datasets is a key challenge in single-cell research. However, existing methods suffer from several fundamental and under-appreciated limitations. First, we do not have a rigorous statistical test for determining whether two single-cell datasets should even be integrated. Moreover, popular methods substantially distort the data during alignment, making the downstream analysis subject to bias and difficult to interpret. We address both challenges with a unified spectral manifold alignment and inference (SMAI) framework. SMAI is a flexible and interpretable method for aligning datasets with the same type of features, equipped with an alignability test justified by statistical theory. It preserves within-data structures and improves downstream analyses, such as identification of differentially expressed genes and imputation of spatial transcriptomics

Bio:Rong Ma is an Assistant Professor of Biostatistics at Harvard T.H. Chan School of Public Health. He received his PhD in biostatistics from the University of Pennsylvania and was a postdoctoral scholar in statistics at Stanford University. His current research focuses on statistical inference for large random matrices, spectral methods, manifold learning, and applications in biomedical sciences, especially in single-cell integrative genomics and multiomics. He was a recipient of the 2022 Lawrence D. Brown Ph.D. Student Award from the Institute of Mathematical Statistics.

Speaker: Rong Ma, HSPH Professor

When: Monday, May 13, 3:00 PM ET

Topic: Principled and interpretable alignability testing and integration of single-cell data

Hybrid: Countway L1-032, Click here

• Dr. Ma will be staying after for those interested in a more in depth, post-seminar discussion of topics reviewed.

Abstract: Aligning and integrating different datasets is a key challenge in single-cell research. However, existing methods suffer from several fundamental and under-appreciated limitations. First, we do not have a rigorous statistical test for determining whether two single-cell datasets should even be integrated. Moreover, popular methods substantially distort the data during alignment, making the downstream analysis subject to bias and difficult to interpret. We address both challenges with a unified spectral manifold alignment and inference (SMAI) framework. SMAI is a flexible and interpretable method for aligning datasets with the same type of features, equipped with an alignability test justified by statistical theory. It preserves within-data structures and improves downstream analyses, such as identification of differentially expressed genes and imputation of spatial transcriptomics

Bio:Rong Ma is an Assistant Professor of Biostatistics at Harvard T.H. Chan School of Public Health. He received his PhD in biostatistics from the University of Pennsylvania and was a postdoctoral scholar in statistics at Stanford University. His current research focuses on statistical inference for large random matrices, spectral methods, manifold learning, and applications in biomedical sciences, especially in single-cell integrative genomics and multiomics. He was a recipient of the 2022 Lawrence D. Brown Ph.D. Student Award from the Institute of Mathematical Statistics.