IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0206608.html
   My bibliography  Save this article

Regularization and grouping -omics data by GCA method: A transcriptomic case

Author

Listed:
  • Monika Piwowar
  • Kinga A Kocemba-Pilarczyk
  • Piotr Piwowar

Abstract

The paper presents the application of Grade Correspondence Analysis (GCA) and Grade Correspondence Cluster Analysis (GCCA) for ordering and grouping -omics datasets, using transcriptomic data as an example. Based on gene expression data describing 256 patients with Multiple Myeloma it was shown that the GCA method could be used to find regularities in the analyzed collections and to create characteristic gene expression profiles for individual groups of patients. GCA iteratively permutes rows and columns to maximize the tau-Kendall or rho-Spearman coefficients, which makes it possible to arrange rows and columns in such a way that the most similar ones remain in each other’s neighbourhood. In this way, the GCA algorithm highlights regularities in the data matrix. The ranked data can then be grouped using the GCCA method, and after that aggregated in clusters, providing a representation that is easier to analyze–especially in the case of large sets of gene expression profiles. Regularization of transcriptomic data, which is presented in this manuscript, has enabled division of the data set into column clusters (representing genes) and row clusters (representing patients). Subsequently, rows were aggregated (based on medians) to visualise the gene expression profiles for patients with Multiple Myeloma in each collection. The presented analysis became the starting point for characterisation of differentiated genes and biochemical processes in which they are involved. GCA analysis may provide an alternative analytical method to support differentiation and analysis of gene expression profiles characterising individual groups of patients.

Suggested Citation

  • Monika Piwowar & Kinga A Kocemba-Pilarczyk & Piotr Piwowar, 2018. "Regularization and grouping -omics data by GCA method: A transcriptomic case," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0206608
    DOI: 10.1371/journal.pone.0206608
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206608
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0206608&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0206608?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kowalczyk, Teresa, 2000. "Link between grade measures of dependence and of separability in pairs of conditional distributions," Statistics & Probability Letters, Elsevier, vol. 46(4), pages 371-379, February.
    2. Wieslaw Szczesny, 1991. "On the performance of a discriminant function," Journal of Classification, Springer;The Classification Society, vol. 8(2), pages 201-215, December.
    3. Monika Piwowar & Wiktor Jurkowski, 2015. "ONION: Functional Approach for Integration of Lipidomics and Transcriptomics Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    4. Cruz-Cano, Raul & Lee, Mei-Ling Ting, 2014. "Fast regularized canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 88-100.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Flavia Esposito, 2021. "A Review on Initialization Methods for Nonnegative Matrix Factorization: Towards Omics Data Experiments," Mathematics, MDPI, vol. 9(9), pages 1-17, April.
    2. Cezary Turek & Sonia Wróbel & Monika Piwowar, 2020. "OmicsON – Integration of omics data with molecular networks and statistical procedures," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-13, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cezary Turek & Sonia Wróbel & Monika Piwowar, 2020. "OmicsON – Integration of omics data with molecular networks and statistical procedures," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-13, July.
    2. Tenenhaus, Arthur & Philippe, Cathy & Frouin, Vincent, 2015. "Kernel Generalized Canonical Correlation Analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 114-131.
    3. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2017. "Electricity forecasting on the individual household level enhanced based on activity patterns," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-26, April.
    4. Kowalczyk, Teresa, 2000. "Link between grade measures of dependence and of separability in pairs of conditional distributions," Statistics & Probability Letters, Elsevier, vol. 46(4), pages 371-379, February.
    5. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    6. Ciok, Alicja, 1996. "An estimator of Gini Separation Index which is nearly median-unbiased," Computational Statistics & Data Analysis, Elsevier, vol. 21(5), pages 533-546, May.
    7. Kowalczyk, T. & Niewiadomska-Bugaj, M., 2001. "An algorithm for maximizing Kendall's [tau]," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 181-193, August.
    8. Kowalczyk, T. & Niewiadomska-Bugaj, M., 2000. "Decomposition of Kendall's [tau]: implications for clustering," Statistics & Probability Letters, Elsevier, vol. 48(4), pages 375-383, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0206608. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.