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Activity Number:
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527
- Novel Statistical Methods for Single-Cell Genomic Data
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Type:
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Invited
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Date/Time:
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Thursday, August 11, 2022 : 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 #320345
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Title:
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ScINSIGHT for Interpreting Single-Cell Gene Expression from Biologically Heterogeneous Data
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Author(s):
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Kun Qian and Shiwei Fu and Hongwei Li and Wei Vivian Li*
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Companies:
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China University of Geosciences and Rutgers, The State University of New Jersey and China University of Geosciences and University of California, Riverside
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Keywords:
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Single-cell genomics;
scRNA-seq;
Data Integration;
Matrix factorization;
Clustering
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Abstract:
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The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for het-erogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.
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Authors who are presenting talks have a * after their name.
