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Using Local Principal Components to Explore Relationships Between Heterogeneous Omics Datasets

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Abstract

In the post-genomic era, high-throughput technologies lead to the generation of large amounts of ‘omics’ data such as transcriptomics, metabolomics, proteomics or metabolomics, that are measured on the same set of samples. The development of methods that are capable to perform joint analysis of multiple datasets from different technology platforms to unravel the relationships between different biological functional levels becomes crucial. A common way to analyze the relationships between a pair of data sources based on their correlation is canonical correlation analysis (CCA). CCA seeks for linear combinations of all the variables from each dataset which maximize the correlation between them. However, in high dimensional datasets, where the number of variables exceeds the number of experimental units, CCA may not lead to meaningful information. Moreover, when collinearity exists in one or both the datasets, CCA may not be applicable. Here, we present a novel method, (LPC-KR), to extract common features from a pair of data sources using Local Principal Components and Kendall’s Ranking. The results show that the proposed algorithm outperforms CCA in many scenarios and is more robust to noisy data. Moreover, meaningful results are obtained using the proposed algorithm when the number of variables exceeds the number of experimental units.

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Authors and Affiliations

  1. Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, 48202, USA

    Noor Alaydie & Farshad Fotouhi

Authors
  1. Noor Alaydie
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  2. Farshad Fotouhi
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Correspondence to Noor Alaydie .

Editor information

Editors and Affiliations

  1. TOBB University Department of Computer Engineering, Sogutozu Ankara, Turkey

    Tansel Özyer

  2. Department of Electrical Engineering Thompson Engineering, University of West Ontario, London, Ontario, Canada

    Keivan Kianmehr

  3. Tobb Etü Economics and Technology Univer, Ankara, Ankara, Turkey

    Mehmet Tan

  4. Baylor College of Medicine, Houston, Texas, USA

    Jia Zeng

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Alaydie, N., Fotouhi, F. (2013). Using Local Principal Components to Explore Relationships Between Heterogeneous Omics Datasets. In: Özyer, T., Kianmehr, K., Tan, M., Zeng, J. (eds) Information Reuse and Integration in Academia and Industry. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1538-1_11

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  • DOI: https://doi.org/10.1007/978-3-7091-1538-1_11

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  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1537-4

  • Online ISBN: 978-3-7091-1538-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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