Activity Number:
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351
- Statistical Methods for Single-Cell Genomics
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #306624
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Title:
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Single-Cell Transcriptome and Regulome Data Integration
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Author(s):
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Weiqiang Zhou* and Zhicheng Ji and Weixiang Fang and Hongkai Ji
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Companies:
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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
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Keywords:
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single-cell genomics;
genomics;
single-cell RNA-seq;
single-cell ATAC-seq;
data integration
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Abstract:
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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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Authors who are presenting talks have a * after their name.