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International Conference on Research in Computational Molecular Biology

RECOMB 2022: Research in Computational Molecular Biology pp 353–355Cite as

A Novel Matrix Factorization Model for Interpreting Single-Cell Gene Expression from Biologically Heterogeneous Data

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13278))

Abstract

Single-cell RNA sequencing (scRNA-seq) technologies enable gene expression measurement at a single-cell resolution, and have opened a new frontier to understand animal development, physiology, and disease-associated molecular mechanisms [1, 2].

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References

  1. Luecken, M.D., Theis, F.J.: Current best practices in single-cell RNA-SEQ analysis: a tutorial. Mol. Syst. Biol. 15(6), e8746 (2019)

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  2. Potter, S.S.: Single-cell RNA sequencing for the study of development, physiology and disease. Nat. Rev. Nephrol. 14(8), 479–492, e8746 (2018)

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  3. Brennecke, P., et al.: Accounting for technical noise in single-cell RNA-SEQ experiments. Nat. Methods 10(11), 1093 (2013)

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  4. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)

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Author information

Authors and Affiliations

  1. School of Mathematics and Physics, China University of Geosciences, Wuhan, Hubei, 430074, China

    Kun Qian & Hongwei Li

  2. Department of Biostatistics and Epidemiology, Rutgers, The State University of New Jersey, Piscataway, 08854, USA

    Shiwei Fu & Wei Vivian Li

Authors
  1. Kun Qian
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  2. Shiwei Fu
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  3. Hongwei Li
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  4. Wei Vivian Li
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Corresponding author

Correspondence to Wei Vivian Li .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Itsik Pe'er

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Qian, K., Fu, S., Li, H., Li, W.V. (2022). A Novel Matrix Factorization Model for Interpreting Single-Cell Gene Expression from Biologically Heterogeneous Data. In: Pe'er, I. (eds) Research in Computational Molecular Biology. RECOMB 2022. Lecture Notes in Computer Science(), vol 13278. Springer, Cham. https://doi.org/10.1007/978-3-031-04749-7_25

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  • DOI: https://doi.org/10.1007/978-3-031-04749-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04748-0

  • Online ISBN: 978-3-031-04749-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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