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Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data

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Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10955))

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Abstract

Non-negative Matrix Factorization (NMF) is recognized as one of fundamentally important and highly popular methods for clustering and feature selection, and many related methods have been proposed so far. Nevertheless, their performances, especially on real data, are still unclear due to few studies focusing on their comparison. This study aims at a assessment study of several representative methods from clustering and feature selection, including NMF, GNMF, MD-NMF, L2,1NMF, LNMF, Convex-NMF and Semi-NMF, on the data of the Cancer Genome Atlas (TCGA), which is one of current research hotspot of bioinformatics. Specifically, three data types of four cancers are either separately or integratedly decomposed as the coefficient matrices and the basis matrices by these NMF methods. The coefficient matrices are evaluated by accuracies of clustered samples and the basis matrices are assessed by p-values of selected genes. Experiment results not only show merits and limitations of compared NMF methods, which may provide guidelines for applying them and proposing novel NMF methods, but also reveal several clues for the exploration of related cancers.

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Acknowledgement

This work was supported in part by the NSFC under grant Nos. 61572284, 61502272 and 61702299.

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Authors and Affiliations

  1. School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China

    Mi-Xiao Hou, Jin-Xing Liu, Junliang Shang, Xiang-Zhen Kong & Ling-Yun Dai

  2. Library of Qufu Normal University, Qufu Normal University, Rizhao, 276826, China

    Ying-Lian Gao

Authors
  1. Mi-Xiao Hou
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  2. Jin-Xing Liu
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  3. Junliang Shang
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  4. Ying-Lian Gao
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  5. Xiang-Zhen Kong
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  6. Ling-Yun Dai
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Corresponding authors

Correspondence to Jin-Xing Liu or Junliang Shang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Wuhan University of Science and Technology, Wuhan City, China

    Xiao-Long Zhang

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Hou, MX., Liu, JX., Shang, J., Gao, YL., Kong, XZ., Dai, LY. (2018). Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_50

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  • DOI: https://doi.org/10.1007/978-3-319-95933-7_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95932-0

  • Online ISBN: 978-3-319-95933-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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