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Sci Rep. 2017 Jun 23;7(1):4162. doi: 10.1038/s41598-017-04070-4.

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

Author information

1
Department of Microbiology, Immunology and Biochemistry, Memphis, TN, 38163, USA. anindyamail123@gmail.com.
2
Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA. anindyamail123@gmail.com.
3
Department of Computer Science and Engineering, University of California, San Diego, CA, 92093, USA. anindyamail123@gmail.com.
4
Department of Microbiology, Immunology and Biochemistry, Memphis, TN, 38163, USA. ycui2@uthsc.edu.
5
Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA. ycui2@uthsc.edu.

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.

PMID:
28646174
PMCID:
PMC5482832
DOI:
10.1038/s41598-017-04070-4
[Indexed for MEDLINE]
Free PMC Article

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