Characterizing the Brain Regulatory Grammar at a Single- cell Resolution to Highlight Disease-driving Genetic and Epigenetic Dysregulations

The UCI Department of Statistics is proud to present Jing Zhang, Assistant Professor, Department of Computer Science, UCI. The public may join the seminars via Zoom at the links below. For additional information, please contact Seminar Administrative Coordinator: Lisa Stieler at lstieler@uci.edu

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 1.3 million single nuclei from post-mortem brain samples and discovered key genetic and epigenetic changes in brain disorders.