Show simple item record

dc.contributor.authorSharma, Alok
dc.contributor.authorKamola, Piotr J
dc.contributor.authorTsunoda, Tatsuhiko
dc.date.accessioned2018-02-19T23:55:48Z
dc.date.available2018-02-19T23:55:48Z
dc.date.issued2017
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-017-1970-8
dc.identifier.urihttp://hdl.handle.net/10072/369839
dc.description.abstractBackground: Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different underlying mechanism, risk prognosis and treatment response. However, biological datasets are usually characterized by a combination of low sample number and very high dimensionality, something that is not adequately addressed by current algorithms. While the performance of the methods is satisfactory for low dimensional data, increasing number of features results in either deterioration of accuracy or inability to cluster. To tackle these challenges, new methodologies designed specifically for such data are needed. Results: We present 2D–EM, a clustering algorithm approach designed for small sample size and high-dimensional datasets. To employ information corresponding to data distribution and facilitate visualization, the sample is folded into its two-dimension (2D) matrix form (or feature matrix). The maximum likelihood estimate is then estimated using a modified expectation-maximization (EM) algorithm. The 2D–EM methodology was benchmarked against several existing clustering methods using 6 medically-relevant transcriptome datasets. The percentage improvement of Rand score and adjusted Rand index compared to the best performing alternative method is up to 21.9% and 155.6%, respectively. To present the general utility of the 2D–EM method we also employed 2 methylome datasets, again showing superior performance relative to established methods. Conclusions: The 2D–EM algorithm was able to reproduce the groups in transcriptome and methylome data with high accuracy. This build confidence in the methods ability to uncover novel disease subtypes in new datasets. The design of 2D–EM algorithm enables it to handle a diverse set of challenging biomedical dataset and cluster with higher accuracy than established methods. MATLAB implementation of the tool can be freely accessed online (http://www.riken.jp/en/research/labs/ims/med_sci_math or http://www.alok-ai-lab.com/).
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBioMed Central
dc.relation.ispartofpagefrom195
dc.relation.ispartofpageto209
dc.relation.ispartofissue547
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume18(Suppl 16)
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.title2D–EM clustering approach for high-dimensional data through folding feature vectors
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, School of Engineering and Built Environment
gro.rights.copyright© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record