dc.contributor.author | Sharma, Alok | |
dc.contributor.author | Kamola, Piotr J | |
dc.contributor.author | Tsunoda, Tatsuhiko | |
dc.date.accessioned | 2018-02-19T23:55:48Z | |
dc.date.available | 2018-02-19T23:55:48Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1471-2105 | |
dc.identifier.doi | 10.1186/s12859-017-1970-8 | |
dc.identifier.uri | http://hdl.handle.net/10072/369839 | |
dc.description.abstract | Background: 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.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | BioMed Central | |
dc.relation.ispartofpagefrom | 195 | |
dc.relation.ispartofpageto | 209 | |
dc.relation.ispartofissue | 547 | |
dc.relation.ispartofjournal | BMC Bioinformatics | |
dc.relation.ispartofvolume | 18(Suppl 16) | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Biological sciences | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 31 | |
dc.title | 2D–EM clustering approach for high-dimensional data through folding feature vectors | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | http://creativecommons.org/licenses/by/4.0/ | |
dc.description.version | Version of Record (VoR) | |
gro.faculty | Griffith 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.hasfulltext | Full Text | |
gro.griffith.author | Sharma, Alok | |