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从多个时间序列数据集中推断成对的调控关系。

Inferring pairwise regulatory relationships from multiple time series datasets.

作者信息

Shi Yanxin, Mitchell Tom, Bar-Joseph Ziv

机构信息

Machine Learning Department, Language Technologies Institute, Computer Science Department and Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.

出版信息

Bioinformatics. 2007 Mar 15;23(6):755-63. doi: 10.1093/bioinformatics/btl676. Epub 2007 Jan 19.

Abstract

MOTIVATION

Time series expression experiments have emerged as a popular method for studying a wide range of biological systems under a variety of conditions. One advantage of such data is the ability to infer regulatory relationships using time lag analysis. However, such analysis in a single experiment may result in many false positives due to the small number of time points and the large number of genes. Extending these methods to simultaneously analyze several time series datasets is challenging since under different experimental conditions biological systems may behave faster or slower making it hard to rely on the actual duration of the experiment.

RESULTS

We present a new computational model and an associated algorithm to address the problem of inferring time-lagged regulatory relationships from multiple time series expression experiments with varying (unknown) time-scales. Our proposed algorithm uses a set of known interacting pairs to compute a temporal transformation between every two datasets. Using this temporal transformation we search for new interacting pairs. As we show, our method achieves a much lower false-positive rate compared to previous methods that use time series expression data for pairwise regulatory relationship discovery. Some of the new predictions made by our method can be verified using other high throughput data sources and functional annotation databases.

AVAILABILITY

Matlab implementation is available from the supporting website: http://www.cs.cmu.edu/~yanxins/regulation_inference/index.html.

摘要

动机

时间序列表达实验已成为在各种条件下研究广泛生物系统的一种流行方法。此类数据的一个优势是能够使用时间滞后分析来推断调控关系。然而,由于时间点数量少且基因数量多,在单个实验中进行这种分析可能会导致许多假阳性结果。将这些方法扩展到同时分析多个时间序列数据集具有挑战性,因为在不同的实验条件下生物系统可能表现得更快或更慢,使得难以依赖实验的实际持续时间。

结果

我们提出了一种新的计算模型和相关算法,以解决从具有不同(未知)时间尺度的多个时间序列表达实验中推断时间滞后调控关系的问题。我们提出的算法使用一组已知的相互作用对来计算每两个数据集之间的时间变换。利用这种时间变换,我们搜索新的相互作用对。正如我们所展示的,与之前使用时间序列表达数据进行成对调控关系发现的方法相比,我们的方法实现了低得多的假阳性率。我们方法做出的一些新预测可以使用其他高通量数据源和功能注释数据库进行验证。

可用性

Matlab实现可从支持网站获取:http://www.cs.cmu.edu/~yanxins/regulation_inference/index.html。

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