A0956
Title: Gene regulatory networks analysis from single cell multi-omics data
Authors: Qiuyue Yuan - Clemson University (United States)
Zhana Duren - Clemson University (United States) [presenting]
Abstract: Existing methods for gene regulatory networks (GRNs) inference rely on gene expression data alone, or on lower resolution bulk data. Despite recent integration of ATAC-seq and RNA-seq data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (LIfelong neural Network for GEne Regulation), a machine learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates both atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor (TF) motifs as a manifold regularization. LINGER achieves 4-7-fold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from a reference single-cell multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.
