A0256
Title: Statistical methods for dimension reduction in single-cell genomics leveraging prior from reference data
Authors: Zhixiang Lin - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: The recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity, and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the prior information in existing datasets, may facilitate the analysis of scCAS data. Statistical methods are presented, which leverage the prior information in massive existing bulk chromatin accessibility and annotated scCAS data. The proposed methods simultaneously model (1) the shared biological variation among scCAS data and the reference data and (2) the unique biological variation in scCAS data that identifies distinct subpopulations.
