Date/Time
Date(s) - 01/31/2022
3:00 pm - 4:00 pm
Location
Communicore, C1-004 with simultaneous Zoom broadcast
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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 mapping the local structure of chromatin. How do we discover structure in this high-dimensional data and use it to understand neuronal development? I have tackled this problem at the single-cell level, and aggregated populations. First, I will present Chroma, a Bayesian state-space model to characterize aggregated chromatin information by modeling the duration of functional and accessible chromatin regions. I will introduce hidden semi-Markov models as a biologically plausible assumption to distill regulatory regions from ATAC-seq data sets. Next, I will introduce MultiVI, a deep generative model for the joint analysis of single-cell chromatin and transcriptional information. MultiVI learns an informative low-dimensional representation that accurately reflects both chromatin and transcriptional properties of individual cells. This tool offers a principled method to analyze samples in which single and multiple genomic data modalities are present. Finally, I will show how these tools can be used to map the chromatin developmental landscape of interneurons in the cerebral cortex and to identify key regulators of neuronal development.
Bio: Mariano is an Assistant Investigator at the Allen Institute for Brain Science. Mariano’s research focuses on applying statistical and machine learning methods to decipher how networks of genes and molecules within cells interact to give rise to the cellular diversity observed in the brain and how these cell types degenerate during neurological diseases. Before joining Allen, he was a research scientist at the Center for Computational Biology at the Flatiron Institute, Simons Foundation and at the Broad Institute of Harvard and MIT. Mariano was a visiting scientist at Mike Jordan’s Group at U.C. Berkeley, developing Bayesian nonparametric methods for super-resolution imaging and deep generative models to represent single-cell information. He completed his Ph.D. in Neuroscience at Columbia University in the laboratory of Charles Zuker where he established collaborations with the groups of Liam Paninski, Larry Abbott.