Tikunov Y M, Laptenok S, Hall R D, Bovy A, de Vos R C H
Metabolomics. 2012 Aug;8(4):714-718. doi: 10.1007/s11306-011-0368-2. Epub 2011 Oct 15.
Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.
质量峰对齐（离子级对齐）最近已成为非靶向代谢谱分析中无监督数据分析的一种流行方法。在此，我们展示了MSClust——一种用于分析源自非靶向谱分析的气相色谱-质谱联用（GC-MS）和液相色谱-质谱联用（LC-MS）数据集的软件工具。MSClust使用无监督聚类进行数据约简，并从离子级色谱对齐数据中提取假定的代谢物质谱。该算法基于减法模糊聚类方法，可无监督地确定数据集中代谢物的数量，并能处理重叠质谱中质量峰的不确定归属。此方法完全基于数据中存在的实际信息，不需要任何先前的代谢物知识。MSClust可应用于GC-MS和LC-MS对齐数据集。电子补充材料：本文的在线版本（doi:10.1007/s11306-011-0368-2）包含补充材料，授权用户可获取。
