Abstract
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 (Hordeum vulgare) 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.
Highlights
The systems biology approach requires large-scale experiments in which multiple genetically polymorphic biosources are studied under varying environmental conditions.The number of the studied genotypes may be very large, especially in plant or animal genetic studies, in which large populations of forms created by artificial breeding or collected in nature are used
The scenario of simulations corresponds to a situation in which it is required to compare two experimental variants, each represented by a number of chromatograms corresponding to individual samples
functional principal component analysis (FPCA) and clustering are appropriate for the analysis of any chromatographic data
Summary
The systems biology approach requires large-scale experiments in which multiple genetically polymorphic biosources are studied under varying environmental conditions.The number of the studied genotypes may be very large, especially in plant or animal genetic studies, in which large populations of forms created by artificial breeding or collected in nature are used. The systems biology approach requires large-scale experiments in which multiple genetically polymorphic biosources are studied under varying environmental conditions. In plant research, where the creation of large experimental populations is relatively easy, repeated measurements are performed at several time points of the performed experiment to deepen the understanding of plant metabolism. Analytical techniques have become increasingly precise and the number of technical measurement replications has gradually decreased, the number of biological replications must be sufficient to properly estimate the natural variation. All of this leads to the situation in which the experiments performed have a multifactorial structure and the number of analyzed samples is very large. Many methods of data analysis have been proposed in the literature, for example, for metabolomic data [1,2,3,4], the problem of separating co-eluting compounds in large chromatographic datasets has not, so far, been satisfactorily solved
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