The goal of the PQG Seminar Series is to encourage the exchanging of ideas and promote interaction, collaboration, and research in quantitative genomics. It seeks to further the development and application of quantitative methods, especially for high dimensional data, as well as focus on the training of quantitative genomic scientists.
2024/2025 Seminar Organizers: Rong Ma
Please direct any logistical questions to Amanda King
Note: Harvard Chan School seeks to bring in speakers with a wide range of experiences and perspectives. They’re here to share their own insights; they do not speak for the school or the university.
All PQG seminar meetings for the semester will be held in person unless otherwise noted.
Upcoming Seminar
Tuesday, December 10, 2024
1:00-2:00 PM
Biostats Conference Room 2-426
Qing Nie
Distinguished Professor of Mathematics and Developmental & Cell Biology
UC Irvine
Systems Learning of Single Cells
Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. While single-cell omics data provides an unprecedented opportunity to profile cellular heterogeneity, the technology requires fixing the cells, often leading to a loss of spatiotemporal and cell interaction information. How to reconstruct temporal dynamics from single or multiple snapshots of single-cell omics data? How to recover interactions among cells, for example, cell-cell communication from single-cell gene expression data? I will present a suite of our recently developed computational methods that learn the single-cell omics data as a spatiotemporal and interactive system. Those methods are built on a strong interplay among systems biology modeling, dynamical systems approaches, machine-learning methods, and optimal transport techniques. The tools are applied to various complex biological systems in development, regeneration, and diseases to show their discovery power. Finally, I will discuss the methodology challenges in systems learning of single-cell data.
2024-2025 Dates
September 17, 2024 - Mengjie Chen, University of Chicago
Mengjie Chen
Associate Professor of Medicine
Associate Professor of Human Genetics
University of Chicago
Beyond variability: a novel gene expression stability metric to unveil homeostasis and regulation
A homeostatic cell performs regular functions to maintain internal balance by continuously responding to both internal and external stimuli, a process that often involves transcriptional regulation. Most genes within such cells exhibit transcriptional stability, while a smaller subset may enter a regulatory or compensatory state in response to stimuli. Key candidates for this type of regulation include ‘first responder’ genes, interferons, and heat shock proteins, among others. When these responses accumulate to a certain threshold, they can lead to observable phenotypic changes and, in some cases, pathological outcomes. Therefore, identifying genes with precise regulation within homeostatic cells is crucial.
Existing statistical tools have mainly focused on cells with uniform behaviors, often overlooking the nuanced regulation of genes in specific cell subsets. In this presentation, I will discuss an unexpected journey that starts with modeling zero-inflation in single-cell data and progresses to the introduction of the Gene Homeostasis Z-index—a novel metric for gene expression stability. This index reveals genes undergoing precise regulation within specific cell subsets, offering insights into their roles in cellular adaptation. For example, we discover regulatory patterns for neuropeptides like insulin and somatostatin, which exhibit extreme values in a limited number of cells. These findings highlight the limitations of conventional mean-based approaches and demonstrate how our method provides a more refined understanding of gene expression stability.
October 15, 2024 - Bo Wang, University of Toronto
Lead Scientist of the Artificial Intelligence Team for Peter Munk Cardiac Centre at University Health Network
Assistant Professor, Departments of Computer Science and Laboratory Medicine & Pathobiology
University of Toronto
Building Foundation Models for Single-cell Omics and Imaging
November 12, 2024 - TBD
Dec 10, 2024 - Qing Nie, UC Irvine
Qing Nie
Distinguished Professor of Mathematics and Developmental & Cell Biology
UC Irvine
Systems Learning of Single Cells
Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. While single-cell omics data provides an unprecedented opportunity to profile cellular heterogeneity, the technology requires fixing the cells, often leading to a loss of spatiotemporal and cell interaction information. How to reconstruct temporal dynamics from single or multiple snapshots of single-cell omics data? How to recover interactions among cells, for example, cell-cell communication from single-cell gene expression data? I will present a suite of our recently developed computational methods that learn the single-cell omics data as a spatiotemporal and interactive system. Those methods are built on a strong interplay among systems biology modeling, dynamical systems approaches, machine-learning methods, and optimal transport techniques. The tools are applied to various complex biological systems in development, regeneration, and diseases to show their discovery power. Finally, I will discuss the methodology challenges in systems learning of single-cell data.
Feb 11, 2025 - Jian Ma, Carnegie Mellon University
March 11, 2025 - Iuliana Ionita-Laza, Columbia Mailman School of Public Health
April 8, 2025 - Dr. Smita Krishnaswamy, Yale University
May 13, 2025 - Elham Azizi, Columbia University
Seminar Archive