UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data

Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matri...

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Main Authors:	Wang, Zhenjia, Li, Guojun, Robinson, Robert W., Huang, Xiuzhen
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4802312/

id	pubmed-4802312
recordtype	oai_dc
spelling	pubmed-48023122016-03-23 UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data Wang, Zhenjia Li, Guojun Robinson, Robert W. Huang, Xiuzhen Article Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed. Nature Publishing Group 2016-03-22 /pmc/articles/PMC4802312/ /pubmed/27001340 http://dx.doi.org/10.1038/srep23466 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Wang, Zhenjia Li, Guojun Robinson, Robert W. Huang, Xiuzhen
spellingShingle	Wang, Zhenjia Li, Guojun Robinson, Robert W. Huang, Xiuzhen UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
author_facet	Wang, Zhenjia Li, Guojun Robinson, Robert W. Huang, Xiuzhen
author_sort	Wang, Zhenjia
title	UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
title_short	UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
title_full	UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
title_fullStr	UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
title_full_unstemmed	UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data
title_sort	unibic: sequential row-based biclustering algorithm for analysis of gene expression data
description	Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general trend-preserving biclusters which are the most meaningful substructures hidden in gene expression data remains a highly challenging problem. We found an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified. We tested it on synthetic and real datasets and compared its performance with currently competitive biclustering tools. We found that the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters. The latter was somewhat better at finding narrow biclusters, the task for which it was specifically designed.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4802312/
_version_	1613555868157083648

UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data

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