IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0191758.html
   My bibliography  Save this article

Assessment of data transformations for model-based clustering of RNA-Seq data

Author

Listed:
  • Janelle R Noel-MacDonnell
  • Joseph Usset
  • Ellen L Goode
  • Brooke L Fridley

Abstract

Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson or negative binomial distribution. Little research has been done to assess how data transformations impact Gaussian model-based clustering with respect to clustering performance and accuracy in estimating the correct number of clusters in RNA-Seq data. In this article, we investigate Gaussian model-based clustering performance and accuracy in estimating the correct number of clusters by applying four data transformations (i.e., naïve, logarithmic, Blom, and variance stabilizing transformation) to simulated RNA-Seq data. To do so, an extensive simulation study was carried out in which the scenarios varied in terms of: how genes were selected to be included in the clustering analyses, size of the clusters, and number of clusters. Following the application of the different transformations to the simulated data, Gaussian model-based clustering was carried out. To assess clustering performance for each of the data transformations, the adjusted rand index, clustering error rate, and concordance index were utilized. As expected, our results showed that clustering performance was gained in scenarios where data transformations were applied to make the data appear “more” Gaussian in distribution.

Suggested Citation

  • Janelle R Noel-MacDonnell & Joseph Usset & Ellen L Goode & Brooke L Fridley, 2018. "Assessment of data transformations for model-based clustering of RNA-Seq data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-12, February.
  • Handle: RePEc:plo:pone00:0191758
    DOI: 10.1371/journal.pone.0191758
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191758
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0191758&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0191758?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. Fraley, Chris & Raftery, Adrian, 2007. "Model-based Methods of Classification: Using the mclust Software in Chemometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i06).
    Full references (including those not matched with items on IDEAS)

    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. Marc Pourroy, 2013. "Inflation-Targeting and Foreign Exchange Interventions in Emerging Economies," Post-Print halshs-00881359, HAL.
    2. Abby Flynt & Nema Dean & Rebecca Nugent, 2019. "sARI: a soft agreement measure for class partitions incorporating assignment probabilities," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 303-323, March.
    3. Mai, Feng & Fry, Michael J. & Ohlmann, Jeffrey W., 2018. "Model-based capacitated clustering with posterior regularization," European Journal of Operational Research, Elsevier, vol. 271(2), pages 594-605.
    4. Ching-Lin Hsiao & Ai-Ru Hsieh & Ie-Bin Lian & Ying-Chao Lin & Hui-Min Wang & Cathy S J Fann, 2014. "A Novel Method for Identification and Quantification of Consistently Differentially Methylated Regions," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-11, May.
    5. Mullen, Katharine M. & van Stokkum, Ivo H. M., 2007. "An Introduction to the "Special Volume Spectroscopy and Chemometrics in R"," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i01).
    6. repec:jss:jstsof:18:i01 is not listed on IDEAS
    7. Motegi, Ryosuke & Seki, Yoichi, 2023. "SMLSOM: The shrinking maximum likelihood self-organizing map," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    8. Paula Carroll & Arthur White, 2017. "Identifying Patterns of Learner Behaviour: What Business Statistics Students Do with Learning Resources," INFORMS Transactions on Education, INFORMS, vol. 18(1), pages 1-13, September.
    9. Vega González-Bueso & Juan José Santamaría & Ignasi Oliveras & Daniel Fernández & Elena Montero & Marta Baño & Susana Jiménez-Murcia & Amparo del Pino-Gutiérrez & Joan Ribas, 2020. "Internet Gaming Disorder Clustering Based on Personality Traits in Adolescents, and Its Relation with Comorbid Psychological Symptoms," IJERPH, MDPI, vol. 17(5), pages 1-13, February.
    10. Torben Schubert & Andrea Bonaccorsi & Tasso Brandt & Daniela De Filippo & Benedetto Lepori & Andreas Niederl, 2014. "Is there a European university model? New evidence on national path dependence and structural convergence," Chapters, in: Andrea Bonaccorsi (ed.), Knowledge, Diversity and Performance in European Higher Education, chapter 2, pages iii-iii, Edward Elgar Publishing.
    11. Xuwen Zhu & Volodymyr Melnykov, 2015. "Probabilistic assessment of model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 395-422, December.
    12. Olsen, Jerome & Kasper, Matthias & Kogler, Christoph & Muehlbacher, Stephan & Kirchler, Erich, 2019. "Mental accounting of income tax and value added tax among self-employed business owners," Journal of Economic Psychology, Elsevier, vol. 70(C), pages 125-139.

    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:pone00:0191758. 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.