On Friday, March 6, the Computer Science Department will host its next colloquium for the Spring 2020 semester. Angela Huang (TCNJ Class of 2017) from the University of Pennsylvania will give a technical talk on bioinformatics entitled “A Subspace Clustering Algorithm for Identifying Cell Populations with scRNA-seq”. An abstract of her talk can be found below.
Please join CS faculty and students in Science Complex P101 from 12:30 – 1:30 PM for this talk.
Refreshments will be provided.
Background: With the advent of single-cell RNA-sequencing (scRNA-seq), researchers now have the ability to define cell types from large amounts of transcriptome information. Over the years, various clustering algorithms have been designed. Motivation for such work can be seen in Cell Atlas projects, which aim to depict cell types present in an organism across its stages of development. Currently, most clustering algorithms measure cell-to-cell similarities using distance metrics on the full set of genes (after the optional step of dimension reduction). This requires clusters to be both compact and far enough apart that the algorithms can recover them. However, for developmental data, the clusters may not be compact; cells may be well spread out across developmental trajectories. The clusters may also not be very far apart; the developmental trajectories may intersect.
Methods: In our work, we propose to model each cell population with a single affine subspace, where all cells of the same type share a common set of constraints.
Results: We present an algorithm that leverages this subspace structure and learns a cell-to-cell affinity matrix based on notions of subspace similarity. We simulate scRNA-seq data according to the subspace model and benchmark the performance of our algorithm against pre-existing methods. We further test our algorithm on an in-house C. elegans dataset and other developmental datasets.
Significance: We show how our algorithm is able to recover information on both cell type and developmental time. Lastly, we demonstrate how the subspace model allows us to compactly recover the major genes involved in an organism’s development.
Angela Huang is a Computer Science alumni of CS @ TCNJ. She is currently pursuing her PhD in Computer Science at The University of Pennsylvania. Her research interests are broadly in the areas of Computational Biology and Data Analysis. Prior to graduate school, her interest in Computational Biology grew through her research experiences in Dr. Dimitris Papamichail’s laboratory at TCNJ, Dr. Xin Li’s laboratory at Louisiana State University, and Dr. Michael Brent’s laboratory at Washington University, St. Louis. In her free time, Angela enjoys nature, art, choreography, and exploring new exhibits around Philadelphia.