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Characterizing gene regulatory grammar at a single-cell resolution

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Jing Zhang, PhD
Assistant Professor, Department of Computer Science, UC Irvine

 

Friday, October 18, 2024
11:00am - 12:00pm  
James H. Clark Center, Room S360, 3rd floor next to the Coffee Shop
Zoom link

Abstract

The recent advances in single-cell sequencing technologies provide unprecedented opportunities to decipher the multi-scale gene regulatory grammars at diverse cellular states. Here, we will introduce our computational efforts to decipher cell-type-specific gene regulatory grammar using large-scale single-cell multi-omics data. First, we developed a deep generative model, named SAILER, to learn the low-dimensional latent cell representations from single-cell epigenetic data for accurate cell state characterization. SAILER adopted the conventional encoder-decoder framework and imposed additional constraints for biologically robust cell embeddings invariant to confounding factors. Then, we will introduce DIRECT-NET, an efficient method to discover cis-regulatory elements and construct regulatory networks using single-cell multi-omics data. Unlike existing methods requiring extensive functional genomic data, DIRECT-NET can build cell-type-specific gene regulatory networks from individual genomes without any auxiliary data. Finally, we applied our methods on single cell data and highlighted key genetic and epigenetic changes in brain disorders. 

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