Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.
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DOI:
10.1371/journal.pone.0059795
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发表时间:
2013
期刊:
影响因子:
3.7
通讯作者:
Savage RS
Savage RS
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Darkins R;Cooke EJ;Ghahramani Z;Kirk PD;Wild DL;Savage RS

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我们生活在一个数据丰富的时代。这就需要开发新颖且具有创新性的统计算法,以便从实验数据中获取最大价值。例如,更快的算法使对更大的基因组数据集进行分析变得可行,从而使我们能够扩展前沿统计方法的用途。我们提出了一种随机算法,该算法利用贝叶斯层次聚类(BHC)统计方法加速时间序列数据的聚类。BHC是一种对任何离散采样的时间序列数据进行聚类的通用方法。在本文中,我们专注于微阵列基因表达数据的一种特定应用。在给出合成数据和真实生物数据集的结果之前,我们对随机算法进行了定义和分析。我们表明,随机算法在聚类质量损失极小的情况下大幅提高了速度。随机时间序列BHC算法作为R包BHC的一部分可供使用,可通过http://bioconductor.org/packages/2.10/bioc/html/BHC.html从Bioconductor(2.10版及以上)下载。我们还提供了一组R脚本,可用于重现本文中进行的分析。这些脚本可从以下网址获取:https://sites.google.com/site/randomisedbhc/
We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.
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