Dr. William Noble, Professor, Department of Genome Sciences, University of Washington
In this talk, I will describe several recent efforts in my lab to help facilitate the analysis of large, single-cell genomics datasets. First is an unsupervised learning method that aims to find shared manifold structure in so-called “multi-omics” datasets, where different aliquots of a sample of cells are subjected to multiple types of single-cell analysis. The second is a discrete optimization method that aims to extract a “sketch,” or representative subset, from a given, big single-cell dataset. Finally, I will describe a deep learning workflow for scRNA-seq analysis, called ACE, that performs a single optimization to project the data to a latent space, cluster the cells in that space, and identify sets of marker genes that explain the differences among the discovered clusters.
William Stafford Noble is a Professor in the Department of Genome Sciences and in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He received the Ph.D. in computer science and cognitive science from University of California, San Diego in 1998. Dr. Noble’s research applies statistical and machine learning methods to the analysis of complex biological data sets. He is the author of more than 250 peer reviewed publications and has advised 29 postdoctoral fellows and 18 PhD students. William is the recipient of the International Society for Computational Biology Innovator award, is on the Clarivate Analytics list of “Highly cited researchers,” and is a Fellow and former member of the Board of Directors of the ISCB.