Associate Software Engineer, Systems Biology Group, Center for Computational Biology.
Title: Inferring Regulatory Dynamics from Pseudotemporally Ordered Single Cell Data
Abstract: Single-cell RNA-seq and ATAC-seq provide a snapshot of the transcriptomic and epigenetic state of individual cells. If a cell population is undergoing a dynamic process, pseudotemporal ordering aims to arrange individual cells by their relative progression through the process. In this talk, I will discuss the development of the codename “Trajectorator,” a novel method to infer regulatory dynamics impacting cellular trajectory from pseudotemporally ordered single-cell data. This approach builds on previous work of researchers at the Flatiron Institute and collaborating institutions, drawing on techniques in trajectory inference, compositional data analysis, variational autoencoders, and network prior construction.