A0874
Title: Inference of single-cell gene regulatory networks via statistical learning methods
Authors: Min Chen - University of Texas at Dallas (United States) [presenting]
Abstract: Gene regulatory networks (GRNs) are crucial for understanding the complex relationships between genes and their regulators, which are fundamental to all cellular processes. By deciphering GRNs, scientists can gain insights into the regulatory crosstalk that drives various diseases, potentially leading to new treatments and therapies. Inferring gene regulatory networks using single-cell RNA sequencing (scRNA-seq) is particularly important because scRNA-seq provides a high-resolution view of gene expression in individual cells, revealing the heterogeneity within a population that bulk RNA-seq might miss. Most existing methods are developed for bulk mRNA experiments. The existing methods are reviewed and demonstrated through simulation and real data the power of statistical learning methods in reconstructing gene regulation networks for single-cell data.
