Condensed Matter Seminar: Inferring collective spatiotemporal organization in biological systems from single-cell data

"Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contain rich, 'hidden' information about biological state that is lost during the experiment or not directly accessible, including cell type, cell cycle phase, gene regulatory patterns and location within the tissue-of-origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. In this talk, I will discuss our recent advancements, based on a combination of spectral, machine learning, and dynamical systems approaches, in probabilistically reconstructing different spatiotemporal signals, filtering them, and disentangling them from each other based on diverse types of prior information, such as topological priors. "