Fast randomization of large genomic datasets while preserving alteration counts.

Fast randomization of large genomic datasets while preserving alteration counts.
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DOI:
10.1093/bioinformatics/btu474
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发表时间:
2014-09-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
通讯作者:
Saez-Rodriguez J
Saez-Rodriguez J
中科院分区:
其他
文献类型:
--
作者:
Gobbi A;Iorio F;Dawson KJ;Wedge DC;Tamborero D;Alexandrov LB;Lopez-Bigas N;Garnett MJ;Jurman G;Saez-Rodriguez J

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Motivation: Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a ‘mutually exclusive’ manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive. Results: We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks. Availability and implementation: BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.html Contact: iorio@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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