从iTRAQ质谱（LC-MS/MS）数据中挖掘时间模式
Mining Temporal Patterns from iTRAQ Mass Spectrometry(LC-MS/MS) Data
摘要：
大规模蛋白质组学分析正在成为生物学中的一项强大技术，并且在很大程度上依赖于由最先进的质谱仪获得的数据。与系统生物学中的任何其他领域一样，需要计算工具来处理这一数据海洋。iTRAQ（Isobaric Tags for Relative and Absolute quantification）是一种可同时定量多个样品中蛋白质的技术。虽然iTRAQ数据为生物学家提供了有用的见解，但由于其多重设计，执行分析和得出生物学结论更加复杂。一个这样的问题是找到在不同时间点之间以类似方式表现（即丰度变化）的蛋白质，因为蛋白质组学数据中的时间变化揭示了重要的生物信息。基于距离的方法，如欧氏距离或皮尔逊系数，和聚类技术，如k-均值等，是不能考虑到时间的信息的序列。在本文中，我们提出了一个线性时间算法聚类相似的模式之间的各种iTRAQ的时间过程数据，无论其绝对值。该算法被称为时间模式挖掘（TPM），将数据从笛卡尔平面映射到离散二进制平面。在映射之后，动态编程技术允许挖掘在时间上彼此更接近的类似数据元素。所提出的算法准确地聚类iTRAQ数据，时间上彼此接近，准确率超过99%。不同规模的问题的实验结果进行了分析，在质量的集群，执行时间和可扩展性的大型数据集。我们的蛋白质组学数据的一个例子是在年底提供展示的算法的性能和聚类时间序列的能力，而不管它们之间的距离。
英文摘要：
Large-scale proteomic analysis is emerging as a powerful technique in biology and relies heavily on data acquired by state-of-the-art mass spectrometers. As with any other field in Systems Biology, computational tools are required to deal with this ocean of data. iTRAQ (isobaric Tags for Relative and Absolute quantification) is a technique that allows simultaneous quantification of proteins from multiple samples. Although iTRAQ data gives useful insights to the biologist, it is more complex to perform analysis and draw biological conclusions because of its multi-plexed design. One such problem is to find proteins that behave in a similar way (i.e. change in abundance) among various time points since the temporal variations in the proteomics data reveal important biological information. Distance based methods such as Euclidian distance or Pearson coefficient, and clustering techniques such as k-mean etc, are not able to take into account the temporal information of the series. In this paper, we present an linear-time algorithm for clustering similar patterns among various iTRAQ time course data irrespective of their absolute values. The algorithm, referred to as Temporal Pattern Mining(TPM), maps the data from a Cartesian plane to a discrete binary plane. After the mapping a dynamic programming technique allows mining of similar data elements that are temporally closer to each other. The proposed algorithm accurately clusters iTRAQ data that are temporally closer to each other with more than 99% accuracy. Experimental results for different problem sizes are analyzed in terms of quality of clusters, execution time and scalability for large data sets. An example from our proteomics data is provided at the end to demonstrate the performance of the algorithm and its ability to cluster temporal series irrespective of their distance from each other.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
