一种高效的大规模质谱数据聚类算法
An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data
摘要：
高通量光谱仪能够产生包含单个生物样品的数千个光谱的数据集。这些数据集包含来自肽的大量冗余，这些肽可能在LC-MS/MS实验中被多次选择。在本文中，我们提出了一个有效的算法，CAMS（聚类算法质谱）的聚类质谱数据，提高了灵敏度和信心的光谱分配。CAMS利用一种称为F-set的新型度量标准，可以准确识别相似的光谱。一个图论框架的定义，允许使用F-集度量有效地准确的聚类识别。该算法的准确性进行了测试的真实HCD和CID数据集与不同量的肽。我们的实验表明，该算法是能够聚类光谱具有非常高的精度在一个合理的时间量为大的光谱数据集。因此，该算法能够通过压缩数据集来减少计算时间，同时通过解释低S/N光谱来增加数据的吞吐量。
英文摘要：
High-throughput spectrometers are capable of producing data sets containing thousands of spectra for a single biological sample. These data sets contain a substantial amount of redundancy from peptides that may get selected multiple times in a LC-MS/MS experiment. In this paper, we present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data which increases both the sensitivity and confidence of spectral assignment. CAMS utilizes a novel metric, called F-set, that allows accurate identification of the spectra that are similar. A graph theoretic framework is defined that allows the use of F-set metric efficiently for accurate cluster identifications. The accuracy of the algorithm is tested on real HCD and CID data sets with varying amounts of peptides. Our experiments show that the proposed algorithm is able to cluster spectra with very high accuracy in a reasonable amount of time for large spectral data sets. Thus, the algorithm is able to decrease the computational time by compressing the data sets while increasing the throughput of the data by interpreting low S/N spectra.
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