Alignment and Integrative Analysis of Single-Cell RNA-Seq and Single-Cell ATAC-Seq Data

• Activity Number: 374 - Statistics in Biosciences (SIB) Special Invited Session – Impacts of Statistics in Genomics and Imaging
• Type: Invited
• Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Weiqiang Zhou and Zhicheng Ji and Hongkai Ji*
• 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; Big data; Data integration; High-throughput sequencing; Prediction; Gene regulation

Abstract

A systems-level understanding of gene regulation requires information from both the transcriptome (i.e., gene expression) and regulome (i.e., cis-regulatory element activities). Single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) are new technologies capable of measuring transcriptome and regulome of individual cells. Typically, the same cell is analyzed by only one technology. In order to collect both transcriptome and regulome data, researchers often apply these two technologies to different cells sampled from the same cell population. A crucial analytical challenge is how to integrate these two data types when they are collected from different cells. We present a method to align scRNA-seq and scATAC-seq data to facilitate such integration. Our method utilizes our recently developed big data regression method BIRD to predict scATAC-seq from scRNA-seq. The predictions are then used as a bridge to connect and align scRNA-seq and scATAC-seq. We illustrate our method using single cell data from the Human Cell Atlas. We show that our method enables coupling of the two data types for decoding gene regulation.