A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules

Abstract In the analysis of large-scale gene expression data, it is important to identify groups of genes with common expression patterns under certain conditions. Many biclustering algorithms have been developed to address this problem. However, comprehensive discovery of functionally coherent bicl...

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Autores principales:	Anindya Bhattacharya, Yan Cui
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d9e8880e632845f5a19149333a3dfca0
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spelling	oai:doaj.org-article:d9e8880e632845f5a19149333a3dfca02021-12-02T12:32:20ZA GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules10.1038/s41598-017-04070-42045-2322https://doaj.org/article/d9e8880e632845f5a19149333a3dfca02017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04070-4https://doaj.org/toc/2045-2322Abstract In the analysis of large-scale gene expression data, it is important to identify groups of genes with common expression patterns under certain conditions. Many biclustering algorithms have been developed to address this problem. However, comprehensive discovery of functionally coherent biclusters from large datasets remains a challenging problem. Here we propose a GPU-accelerated biclustering algorithm, based on searching for the largest Condition-dependent Correlation Subgroups (CCS) for each gene in the gene expression dataset. We compared CCS with thirteen widely used biclustering algorithms. CCS consistently outperformed all the thirteen biclustering algorithms on both synthetic and real gene expression datasets. As a correlation-based biclustering method, CCS can also be used to find condition-dependent coexpression network modules. We implemented the CCS algorithm using C and implemented the parallelized CCS algorithm using CUDA C for GPU computing. The source code of CCS is available from https://github.com/abhatta3/Condition-dependent-Correlation-Subgroups-CCS.Anindya BhattacharyaYan CuiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anindya Bhattacharya Yan Cui A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
description	Abstract In the analysis of large-scale gene expression data, it is important to identify groups of genes with common expression patterns under certain conditions. Many biclustering algorithms have been developed to address this problem. However, comprehensive discovery of functionally coherent biclusters from large datasets remains a challenging problem. Here we propose a GPU-accelerated biclustering algorithm, based on searching for the largest Condition-dependent Correlation Subgroups (CCS) for each gene in the gene expression dataset. We compared CCS with thirteen widely used biclustering algorithms. CCS consistently outperformed all the thirteen biclustering algorithms on both synthetic and real gene expression datasets. As a correlation-based biclustering method, CCS can also be used to find condition-dependent coexpression network modules. We implemented the CCS algorithm using C and implemented the parallelized CCS algorithm using CUDA C for GPU computing. The source code of CCS is available from https://github.com/abhatta3/Condition-dependent-Correlation-Subgroups-CCS.
format	article
author	Anindya Bhattacharya Yan Cui
author_facet	Anindya Bhattacharya Yan Cui
author_sort	Anindya Bhattacharya
title	A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
title_short	A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
title_full	A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
title_fullStr	A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
title_full_unstemmed	A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
title_sort	gpu-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/d9e8880e632845f5a19149333a3dfca0
work_keys_str_mv	AT anindyabhattacharya agpuacceleratedalgorithmforbiclusteringanalysisanddetectionofconditiondependentcoexpressionnetworkmodules AT yancui agpuacceleratedalgorithmforbiclusteringanalysisanddetectionofconditiondependentcoexpressionnetworkmodules AT anindyabhattacharya gpuacceleratedalgorithmforbiclusteringanalysisanddetectionofconditiondependentcoexpressionnetworkmodules AT yancui gpuacceleratedalgorithmforbiclusteringanalysisanddetectionofconditiondependentcoexpressionnetworkmodules
_version_	1718394125954318336

A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules

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