Topic: Modeling Structure in Single-cell Chromatin Data.
In recent years, single-cell technologies have revolutionized biology, providing exciting opportunities to map cellular populations through development. Among these technologies, single-cell ATAC-seq has become the leading assay for probing the cellular regulatory landscape by mapping chromatin information. How do we discover structure in this high-dimensional data and use it to understand development? Here, I will present a Bayesian state space model to characterize chromatin information by modeling the duration of functional and accessible chromatin regions, termed ChromA. I will introduce hidden semi-Markov models as a biologically plausible assumption to distill regulatory regions from ATAC-seq data sets. Next, I will show how this model can be extended to analyze single-cell ATAC-seq information and to compare different cellular populations. Finally, I will show how ChromA can be used to map the chromatin developmental landscape of Interneurons in the cerebral cortex, revealing the fundamental logic of cortical interneuron specification