Department of Electronics Engineering and Information Science, University of Science and Technology of China, PO Box 4, Hefei, Anhui 230027, P.R. China.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul-Aug;8(4):1054-66. doi: 10.1109/TCBB.2009.56.
Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.
峰检测是质谱分析中最重要的步骤之一。然而，检测结果受到严重的光谱变化的极大影响。不幸的是，目前大多数的峰检测方法既不够灵活，无法修正错误的检测结果，也不够稳健，无法抵抗光谱变化。为了提高灵活性，我们引入了峰树来表示 MS 光谱中的峰信息。每个树节点都是对一系列尺度的峰判断，而每个树分解，作为一组节点，是候选峰检测结果。为了提高稳健性，我们将峰检测和常见峰对齐结合到一个闭环框架中，通过峰强度和常见峰信息来找到最佳的分解。常见峰信息是从最新峰检测结果的密度聚类中推导出来的，并进行循环细化。最后，我们提出了一种改进的蚁群优化生物标志物选择方法，以构建整个 MS 分析系统。实验表明，我们的峰检测方法能够更好地抵抗光谱变化，比传统方法具有更高的灵敏度和更低的误报率。我们基于峰树的系统在真实的 SELDI 数据上进行 MS 疾病分析的优势也得到了证明。
