Machine learning meets single-cell biology: Cellular dynamics and gene programs

Single-cell genomics is providing unprecedented views into the workings of cells and their interrelationships. Beginning with the conceptualization of cell states as residing along a phenotypic manifold and its representation as a neighbor graph, we will survey approaches to infer trajectories and cell-state transitions from this data. To understand dynamics along the manifold from snapshot data, we can model differentiation as a Markov process, thereby providing a continuous view of cell fate choices and enabling a quantification of cellular plasticity. We will demonstrate the versatility of this approach, provide cooking tips and pitfalls to avoid, and discuss computational challenges. We will also highlight the importance of understanding gene expression and regulation based on its structure in the data. To identify biologically meaningful gene programs, we leverage Bayesian matrix factorization, combining prior knowledge with data to summarize biological activity as a compact set of factors representing cellular building blocks. This presentation will cover several single-cell algorithms, including Wanderlust, Palantir, CellRank, Decipher and Spectra.

Biography

Dana Pe’er is an HHMI Investigator, Chair of the Computational and Systems Biology Program and Director of the Gerry Metastasis and Tumor Ecosystems Center at the Sloan Kettering Institute. The Pe’er lab has pioneered foundational machine learning approaches to derive cell states, trajectories and interactions from single-cell genomics and imaging data. They apply their tools to address questions in development, immunity and cancer, with a focus on plasticity, tumor heterogeneity, and tumor-immune interactions. Dr. Pe'er earned her PhD at the Hebrew University. She received the Burroughs Wellcome Fund Career Award, NSF CAREER award, NIH Director's New Innovator and Pioneer awards, Packard Fellowship in Science and Engineering, Ernst W. Bertner Memorial Award, ISCB Overton Prize Award and is a Fellow of the AACR Academy. She serves on the editorial board of Cell, leads an NCI Human Tumor Atlas Network center, and heads computational analysis for the Human Cell Atlas.

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