IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0176334.html
   My bibliography  Save this article

shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics

Author

Listed:
  • Bohdan B Khomtchouk
  • James R Hennessy
  • Claes Wahlestedt

Abstract

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, especially across single-cell sequencing studies. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Also, shinyheatmap features a built-in high performance web plug-in, fastheatmap, for rapidly plotting interactive heatmaps of datasets as large as 105—107 rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap. Users can access fastheatmap directly from within the shinyheatmap web interface, and all source code has been made publicly available on Github: https://github.com/Bohdan-Khomtchouk/fastheatmap.

Suggested Citation

  • Bohdan B Khomtchouk & James R Hennessy & Claes Wahlestedt, 2017. "shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-9, May.
  • Handle: RePEc:plo:pone00:0176334
    DOI: 10.1371/journal.pone.0176334
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176334
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0176334&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0176334?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. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    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. Nicola G Criscuolo & Claudia Angelini, 2020. "StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
    2. Laura D Hughes & Scott A Lewis & Michael E Hughes, 2017. "ExpressionDB: An open source platform for distributing genome-scale datasets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
    3. Sara Della Torre & Valeria Benedusi & Giovanna Pepe & Clara Meda & Nicoletta Rizzi & Nina Henriette Uhlenhaut & Adriana Maggi, 2021. "Dietary essential amino acids restore liver metabolism in ovariectomized mice via hepatic estrogen receptor α," Nature Communications, Nature, vol. 12(1), pages 1-13, December.

    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. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Liu, Kailiang & Xu, Zhitong & Chen, Chun-houh & Nakano, Junji & Honda, Keisuke, 2023. "Article’s scientific prestige: Measuring the impact of individual articles in the web of science," Journal of Informetrics, Elsevier, vol. 17(1).
    2. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.

    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:pone00:0176334. 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.