Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks
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DOI：
10.1109/tcbb.2011.62
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发表时间：
2012-03-01
影响因子：
4.5
通讯作者：
Kestler, Hans A.
中科院分区：
文献类型：
--
作者：
Hopfensitz, Martin;Muessel, Christoph;Kestler, Hans A.
网络推理算法可以帮助生命科学家在分子水平上解开基因调控系统。近年来，从时间序列重构布尔网络引起了人们的极大关注。这些都需要二值化，因为这样的网络将基因建模为二进制变量(无论是“表达的”还是“未表达的”)。常见的二值化方法通常根据统计或信息理论特征对测量结果进行聚类或分离，并且可能需要许多数据点来确定稳健的阈值。然而，时间序列测量通常只包含少量样本。为了克服这一局限性，我们提出了一种结合多分辨率测量的二值化方法。我们介绍了两种这样的二值化方法，这两种方法基于有限数量的样本来确定阈值，并另外提供了阈值有效性的度量。因此，网络重建和进一步分析可以限制在具有有意义阈值的基因上。这降低了网络推理的复杂性。在使用人工数据和真实酵母表达时间序列的网络重建实验中，对我们的二值化算法的性能进行了评估。与其他二值化技术相比，新方法有效地减少了候选网络的数量，从而大大提高了正确的网络识别率。
Network inference algorithms can assist life scientists in unraveling gene-regulatory systems on a molecular level. In recent years, great attention has been drawn to the reconstruction of Boolean networks from time series. These need to be binarized, as such networks model genes as binary variables (either "expressed" or "not expressed"). Common binarization methods often cluster measurements or separate them according to statistical or information theoretic characteristics and may require many data points to determine a robust threshold. Yet, time series measurements frequently comprise only a small number of samples. To overcome this limitation, we propose a binarization that incorporates measurements at multiple resolutions. We introduce two such binarization approaches which determine thresholds based on limited numbers of samples and additionally provide a measure of threshold validity. Thus, network reconstruction and further analysis can be restricted to genes with meaningful thresholds. This reduces the complexity of network inference. The performance of our binarization algorithms was evaluated in network reconstruction experiments using artificial data as well as real-world yeast expression time series. The new approaches yield considerably improved correct network identification rates compared to other binarization techniques by effectively reducing the amount of candidate networks.