Sawikowska Aneta, Piasecka Anna, Kachlicki Piotr, Krajewski Paweł
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland.
Institute of Bioorganic Chemistry, Polish Academy of Sciences, Z. Noskowskiego 12/14, 61-704 Poznań, Poland.
Metabolites. 2021 Mar 31;11(4):214. doi: 10.3390/metabo11040214.
Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley () leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.
峰重叠是色谱分析中的一个常见问题，在复杂生物混合物（即代谢物）的情况下尤为突出。由于存在具有相似色谱特性的不同化合物共洗脱现象，峰分离变得具有挑战性。本文描述、比较并通过实验验证了两种首次应用于大型色谱数据集的峰分离计算方法。这些方法从原始观测数据得出可作为统计分析输入的数据。首先，在两种方法中，数据都通过样品质量进行归一化，去除基线，进行保留时间校准，并执行峰检测。然后，在第一种方法中，使用聚类来分离重叠峰，而在第二种方法中，应用功能主成分分析（FPCA）来达到相同目的。以模拟数据和实验结果为例展示并比较了这两种方法。实际数据是在一项关于干旱胁迫下大麦（）叶片代谢组变化的研究中获得的。结果表明，两种方法都适用于分离重叠峰，但FPCA的额外优势在于能够评估不同色谱图中同一峰内存在的各个化合物的变异性。
