Dataset Information

Runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.

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.

SUBMITTER: Orzechowski P

PROVIDER: S-EPMC6289127 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.

Orzechowski Patryk P Panszczyk Artur A Huang Xiuzhen X Moore Jason H JH

Bioinformatics (Oxford, England) 20181201 24

<h4>Motivation</h4>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.<h4>Results</h4>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 ...[more]

PMID: 29939213

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Project description:Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.

Project description:BackgroundThe DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis.ResultsWe develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm.ConclusionWe have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result. In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.

Dataset Information

Runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.

Publications

runibic: a Bioconductor package for parallel row-based biclustering of gene expression data.

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