Single-Cell ATAC-Seq Signal Extraction and Enhancement

• Activity Number: 654 - New Methodology Developments in Single Cell RNA-Seq
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Hongkai Ji* and Zhicheng Ji and Weiqiang Zhou
• Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
• Keywords: single cell genomics; ATAC-seq; data integration; sequencing; hierarchical model

Abstract

Single-cell assay of transposase-accessible chromatin followed by sequencing (scATAC-seq) is an emerging new technology for studying gene regulation. Unlike the conventional ChIP-seq, DNase-seq and ATAC-seq technologies which measure average behavior of a cell population, scATAC-seq measures regulatory element activities within each individual cell, thereby allowing one to examine the heterogeneity of a cell population. Analyzing scATAC-seq data is challenging because the data are highly sparse and discrete. We present a statistical model to effectively extract signals from the noisy scATAC-seq data. Our method leverages information in massive amounts of publicly available DNase-seq data to enhance the scATAC-seq signal. We demonstrate through real data analyses that this approach substantially improves the accuracy for reconstructing genome-wide regulatory element activities.