Patryk Orzechowski1,2, Artur Panszczyk2, Xiuzhen Huang3, Jason H Moore1. 1. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA. 2. Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland. 3. Department of Computer Science, Arkansas State University, Jonesboro, AR, USA.
Abstract
Motivation: Biclustering is an unsupervised technique of simultaneous clustering of rows and columns of input matrix. With multiple biclustering algorithms proposed, UniBic remains one of the most accurate methods developed so far. Results: In this paper we introduce a Bioconductor package called runibic with parallel implementation of UniBic. For the convenience the algorithm was reimplemented, parallelized and wrapped within an R package called runibic. The package includes: (i) a couple of times faster parallel version of the original sequential algorithm, (ii) much more efficient memory management, (iii) modularity which allows to build new methods on top of the provided one and (iv) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSet and biclust. Availability and implementation: The package is implemented in R and is available from Bioconductor (starting from version 3.6) at the following URL http://bioconductor.org/packages/runibic with installation instructions and tutorial. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Biclustering is an unsupervised technique of simultaneous clustering of rows and columns of input matrix. With multiple biclustering algorithms proposed, UniBic remains one of the most accurate methods developed so far. Results: In this paper we introduce a Bioconductor package called runibic with parallel implementation of UniBic. For the convenience the algorithm was reimplemented, parallelized and wrapped within an R package called runibic. The package includes: (i) a couple of times faster parallel version of the original sequential algorithm, (ii) much more efficient memory management, (iii) modularity which allows to build new methods on top of the provided one and (iv) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSet and biclust. Availability and implementation: The package is implemented in R and is available from Bioconductor (starting from version 3.6) at the following URL http://bioconductor.org/packages/runibic with installation instructions and tutorial. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Sepp Hochreiter; Ulrich Bodenhofer; Martin Heusel; Andreas Mayr; Andreas Mitterecker; Adetayo Kasim; Tatsiana Khamiakova; Suzy Van Sanden; Dan Lin; Willem Talloen; Luc Bijnens; Hinrich W H Göhlmann; Ziv Shkedy; Djork-Arné Clevert Journal: Bioinformatics Date: 2010-04-23 Impact factor: 6.937
Authors: Daniel Gusenleitner; Eleanor A Howe; Stefan Bentink; John Quackenbush; Aedín C Culhane Journal: Bioinformatics Date: 2012-07-12 Impact factor: 6.937