Dexter Alex, Race Alan M, Steven Rory T, Barnes Jennifer R, Hulme Heather, Goodwin Richard J A, Styles Iain B, Bunch Josephine
PSIBS Doctoral Training Centre, University of Birmingham Edgbaston, Birmingham B15 2TT, United Kingdom.
National Physical Laboratory, Teddington, Middlesex TW11 0LW, United Kingdom.
Anal Chem. 2017 Nov 7;89(21):11293-11300. doi: 10.1021/acs.analchem.7b01758. Epub 2017 Oct 25.
Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology.
聚类在质谱成像（MSI）中被广泛用于分割解剖特征和区分组织类型，但现有方法既耗费中央处理器（CPU）资源又占用内存，限制了它们在小型单一数据集上的应用。我们提出了一种新方法，该方法使用基于图的算法和两阶段采样方法，克服了这一限制。我们在一系列样本类型上演示了该算法，结果表明它能够分割质谱成像中常用算法无法识别的解剖特征，并且我们在合成质谱成像数据上验证了我们的结果。我们通过使用不同激光能量密度获取的数据成功地对具有设计方差的数据进行聚类，表明该算法对数据质量波动具有鲁棒性。最后，我们表明该方法能够对在最新一代质谱成像仪器上获取的大型质谱成像数据集生成准确的分割结果，并通过与组织病理学比较来评估这些结果。
