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MAGAN: Aligning Biological Manifolds
Matt Amodio · Smita Krishnaswamy

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #166

It is increasingly common in many types of natural and physical systems (especially biological systems) to have different types of measurements performed on the same underlying system. In such settings, it is important to align the manifolds arising from each measurement in order to integrate such data and gain an improved picture of the system; we tackle this problem using generative adversarial networks (GANs). Recent attempts to use GANs to find correspondences between sets of samples do not explicitly perform proper alignment of manifolds. We present the new Manifold Aligning GAN (MAGAN) that aligns two manifolds such that related points in each measurement space are aligned. We demonstrate applications of MAGAN in single-cell biology in integrating two different measurement types together: cells from the same tissue are measured with both genomic (single-cell RNA-sequencing) and proteomic (mass cytometry) technologies. We show that MAGAN successfully aligns manifolds such that known correlations between measured markers are improved compared to other recently proposed models.

Author Information

Matt Amodio (Yale University)
Smita Krishnaswamy (Yale University)

Smita Krishnaswamy is an Assistant Professor in the department of Genetics at the Yale School of Medicine and Department of Computer Science in the Yale School of Applied Science and Engineering. Smita’s research focuses on applying machine learning methods to high-throughput high dimensional biological data. Smita has been focusing on using manifold learning and deep learning to develop unsupervised algorithmic approaches to naturally process data, visualize it, understand progressions , find phenotypic diversity, and infer patterns. Some of the key projects developed in her Lab include MAGIC (a tool for imputation and denoising of data), PHATE (a powerful new visualization method for high dimensional data that can unveil progression and cluster structures, and SAUCIE (an autoencoder-based deep learning approach for automatically batch correcting, visualizing, denoising and clustering data). These methods have been applied to a variety of biological applications including embryoid body differentiation, the epithelial-to-mesenchymal transition in breast cancer, lung cancer immunotherapy, infectious disease data, gut microbiome data and population genetics data. At Yale, Smita teaches two CS/Genetics/Computational Biology cross-listed courses. Advanced Topics in Machine Learning & Data Mining (Spring), and Machine Learning for Biology (Fall). She completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She was trained as a computer scientist with a Ph.D. from the University of Michigan’s EECS department where her research focused on algorithms for automated synthesis and verification of nanoscale logic circuits that exhibit probabilistic effects.

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