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Activity Number: 351 - Statistical Methods for Single-Cell Genomics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306624
Title: Single-Cell Transcriptome and Regulome Data Integration
Author(s): Weiqiang Zhou* and Zhicheng Ji and Weixiang Fang 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 and Johns Hopkins Bloomberg School of Public Health
Keywords: single-cell genomics; genomics; single-cell RNA-seq; single-cell ATAC-seq; data integration
Abstract:

Advantages in single-cell genomic technologies allow us to assay the transcriptome or regulome for hundreds of thousands of individual cells. However, tools for integrating different types of single-cell genomic data are still lacking. Here, we develop a new method for integrating single-cell transcriptome and regulome data. We show that our method outperforms existing methods in matching known cell types between single-cell RNA-seq and single-cell ATAC-seq data. We also applied our method to integrate the currently released bone marrow single-cell RNA-seq data from human cell atlas and publicly available single-cell ATAC-seq data and identified key regulatory pathways in hematopoietic cell development.


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