Activity Number:
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480
- Novel Statistical Methods for Bioinformatics and Computational Biology
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Type:
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Invited
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Date/Time:
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Wednesday, July 31, 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 #300544
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Title:
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Statistical Methods for Single Cell Regulomics
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Author(s):
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Sunduz Keles* and Daniel Conn
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Companies:
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UW Madison and University of Wisconsin
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Keywords:
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scRNA-seq;
networks;
transcription factors;
random forests;
zero inflation;
count data
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Abstract:
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Single-cell RNA-sequencing (scRNA-seq) emerged as a revolutionary tool that enables addressing scientific questions that were elusive just a few years ago. A key area of study with scRNA-seq is coordinated gene expression within regulatory networks. Regulatory networks are critically important for development and function where aberrations have been associated with numerous diseases. While a number of methods have been developed recently for regulatory network reconstruction of single-cell RNA-seq data, these methods heavily rely on methods for bulk RNA-seq. We specifically consider the class of methods that utilize random forests/regression trees as a building block of regulatory networks and benchmark its properties for scRNA-seq data. We propose zero inflated random forests to better characterize the regulatory relationships of transcription factors and their targets. We illustrate the properties of the ZIRF on scRNA-seq study of progenitor blood cells.
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Authors who are presenting talks have a * after their name.