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

Anindya Bhattacharya, Yan Cui

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish (US)
Article number4162
JournalScientific Reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017

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Gene expression
Genes
Graphics processing unit

All Science Journal Classification (ASJC) codes

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A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules. / Bhattacharya, Anindya; Cui, Yan.

In: Scientific Reports, Vol. 7, No. 1, 4162, 01.12.2017.

Research output: Contribution to journalArticle

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