IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v14y2015i3p311-316n7.html
   My bibliography  Save this article

TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists

Author

Listed:
  • Schimek Michael G.

    (Statistical Bioinformatics, IMI, Medical University of Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria)

  • Budinská Eva

    (Bioinformatics in Translational Research, IBA, Masaryk University, Kotlarska 2, 61137 Brno, Czech Republic)

  • Kugler Karl G.

    (Institute for Bioinformatics and Systems Biology, Helmholtz Centre Munich, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany)

  • Švendová Vendula

    (Statistical Bioinformatics, IMI, Medical University of Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria)

  • Ding Jie

    (Stanford Cancer Institute, Stanford University, 265 Campus Drive, Stanford, CA 94305-5456, USA)

  • Lin Shili

    (Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210, USA)

Abstract

High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data. However, there is a lack of biostatistical tools for the integration of results obtained from different technologies. Although diverse technological platforms produce different raw data, one commonality for experiments with the same goal is that all the outcomes can be transformed into a platform-independent data format – rankings – for the same set of items. Here we present the R package TopKLists, which allows for statistical inference on the lengths of informative (top-k) partial lists, for stochastic aggregation of full or partial lists, and for graphical exploration of the input and consolidated output. A graphical user interface has also been implemented for providing access to the underlying algorithms. To illustrate the applicability and usefulness of the package, we integrated microRNA data of non-small cell lung cancer across different measurement techniques and draw conclusions. The package can be obtained from CRAN under a LGPL-3 license.

Suggested Citation

  • Schimek Michael G. & Budinská Eva & Kugler Karl G. & Švendová Vendula & Ding Jie & Lin Shili, 2015. "TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(3), pages 311-316, June.
  • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:3:p:311-316:n:7
    DOI: 10.1515/sagmb-2014-0093
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2014-0093
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/sagmb-2014-0093?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tzu-Pin Lu & Chien-Yueh Lee & Mong-Hsun Tsai & Yu-Chiao Chiu & Chuhsing Kate Hsiao & Liang-Chuan Lai & Eric Y Chuang, 2012. "miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    2. Peter Hall & Michael G. Schimek, 2012. "Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 661-672, June.
    3. Shili Lin & Jie Ding, 2009. "Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies," Biometrics, The International Biometric Society, vol. 65(1), pages 9-18, March.
    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. Švendová, Vendula & Schimek, Michael G., 2017. "A novel method for estimating the common signals for consensus across multiple ranked lists," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 122-135.
    2. Giuseppe Jurman & Samantha Riccadonna & Roberto Visintainer & Cesare Furlanello, 2012. "Algebraic Comparison of Partial Lists in Bioinformatics," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-20, May.
    3. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
    4. Fangyuan Zhang & Jie Ding & Shili Lin, 2017. "Testing for Associations of Opposite Directionality in a Heterogeneous Population," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 137-159, June.
    5. Arboretti, Rosa & Bonnini, Stefano & Corain, Livio & Salmaso, Luigi, 2014. "A permutation approach for ranking of multivariate populations," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 39-57.
    6. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.
    7. Donald Margaret R. & Wilson Susan R., 2017. "Comparison and visualisation of agreement for paired lists of rankings," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 31-45, March.
    8. Antonio Jiménez-Martín & Eduardo Gallego & Alfonso Mateos & Juan A. Fernández Pozo, 2017. "Restoring a Radionuclide Contaminated Aquatic Ecosystem: A Group Decision Making Problem with Incomplete Information within MAUT Accounting for Veto," Group Decision and Negotiation, Springer, vol. 26(4), pages 653-675, July.
    9. Lin Shili, 2010. "Space Oriented Rank-Based Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-25, April.
    10. Ding, Jiankun & Han, Deqiang & Yang, Yi, 2018. "Iterative ranking aggregation using quality improvement of subgroup ranking," European Journal of Operational Research, Elsevier, vol. 268(2), pages 596-612.
    11. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.
    12. Sophie L Wardle & Mark E S Bailey & Audrius Kilikevicius & Dalia Malkova & Richard H Wilson & Tomas Venckunas & Colin N Moran, 2015. "Plasma MicroRNA Levels Differ between Endurance and Strength Athletes," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    13. Luisa Cutillo & Annamaria Carissimo & Silvia Figini, 2012. "Network Selection: A Method for Ranked Lists Selection," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-13, August.
    14. Li, Yumeng & Wang, Ran & Yao, Nian & Zhang, Shuguang, 2017. "A moderate deviation principle for stochastic Volterra equation," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 79-85.

    More about this item

    Keywords

    62G99; 65K10; 68N01; 65C60; 62F07;
    All these keywords.

    JEL classification:

    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:bpj:sagmbi:v:14:y:2015:i:3:p:311-316:n:7. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.