2023/05/03 Amit Moscovich

Amit Moscovich

Assistant Professor, Department of Statistics and Operations Research, Tel Aviv University

Date and time: May 3rd, 2023
Wednesday, at 8am PDT / 11am EDT / 4pm BST / 5pm CEST / 11pm China

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Tools for heterogeneity in cryo-EM: manifold learning, disentanglement and optimal transport

Despite much recent progress, the reconstruction and analysis of macromolecules with continuous conformational heterogeneity remains a key challenge.  In this talk we present two promising tools that can aid in various stages of the reconstruction and analysis pipelines.

In the first part of the talk, the notion of manifold factorization and disentanglement is presented.  This is an approach to dimensionality reduction where different aspects of the data are assigned separate coordinates.  It has potential applications both for the reconstruction of single-particle cryo-EM samples with continuous heterogeneity and for the analysis of the reconstructed volumetric datasets.

In the second part of the talk, we will discuss the potential applications of optimal transport for cryo-EM, in particular for the analysis of heterogeneous samples, as well as for class-averaging and particle picking.  Optimal transport metrics are closely related to physical motion, making them a natural choice for many of the core problems in cryo-EM.  Historically, computational bottlenecks have limited the applicability of optimal transport.  However, recent advances in computational optimal transport have yielded fast approximation schemes that can be readily used for the analysis of high-resolution images and volumetric arrays.