PhD Dissertation Defense: Maya Sheth
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Title: Mapping Enhancer-Gene Regulatory Interactions from Single-Cell Data Abstract: Mapping enhancers and their target genes in specific cell types is essential for understanding gene regulation and the impact of human genetic variation on disease. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here, we introduce a new family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell ATAC-seq or paired RNA/ATAC-seq multiomic data and are trained on a CRISPR perturbation dataset including >10,000 experimentally tested element-gene pairs. To validate scE2G, we benchmark the models against CRISPR perturbations, fine-mapped eQTLs, and GWAS variant-gene associations and demonstrate state-of-the-art predictive performance across multiple cell types and categories of perturbations. Using scE2G, we build regulatory maps in heterogeneous tissues and show how they can be
