Activity Number:
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654
- New Methodology Developments in Single Cell RNA-Seq
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #327226
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Title:
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Single-Cell ATAC-Seq Signal Extraction and Enhancement
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Author(s):
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Hongkai Ji* and Zhicheng Ji and Weiqiang Zhou
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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
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Keywords:
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single cell genomics;
ATAC-seq;
data integration;
sequencing;
hierarchical model
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.