Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale
Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale
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DOI：
10.1101/2021.07.01.450702
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Konstantin Bob;David Teschner;T. Kemmer;David Gomez-Zepeda;S. Tenzer;B. Schmidt;A. Hildebrandt
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文献类型：
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作者：
Konstantin Bob;David Teschner;T. Kemmer;David Gomez-Zepeda;S. Tenzer;B. Schmidt;A. Hildebrandt
质谱技术是蛋白质组学研究中的一项重要实验技术。然而，某些质谱数据的分析面临着两个挑战的组合：第一，即使是单个实验也会产生大量的多维原始数据，第二，感兴趣的信号不是单峰，而是沿着沿着不同维度的峰的模式。快速增长的质谱数据量增加了对可扩展解决方案的需求。现有的信号检测方法通常不适合并行处理大量数据，或者依赖于关于信号特性的强假设。在这项研究中，它表明，局部敏感的哈希使信号分类质谱原始数据的规模。通过适当选择算法参数，可以平衡假阳性率和假阴性率。在合成数据上，实现了与强度阈值方法相比的上级性能。该实现在真实的数据上扩展到88个线程。局部敏感散列法是质谱原始数据信号分类的理想方法。生成的数据和代码可在https://github.com/hildebrandtlab/mzBucket上获得。原始数据见https://zenodo.org/record/5036526。
Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. The implementation scaled out up to 88 threads on real data. Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. Generated data and code are available at https://github.com/hildebrandtlab/mzBucket. Raw data is available at https://zenodo.org/record/5036526.