Abstract

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.

Highlights

  • Metabolomics aims at the unbiased and comprehensive quantification of metabolites in organisms, tissues, or cells [1,2]

  • We extended the KPIC2 with uniform manifold approximation and projection (UMAP) and XGBoost to analyze the complex liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomic datasets

  • The performance of the XGBoost model based on the extraction results of KPIC2 is compared with XCMS

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Summary

Introduction

Metabolomics aims at the unbiased and comprehensive quantification of metabolites in organisms, tissues, or cells [1,2]. The combination of chromatography and mass spectrometry has become the key technology for the analysis of metabolites in biological systems [4,5]. Compaed with gas chromatography coupled to mass spectrometry (GC–MS) [6,7,8], high-performance liquid chromatography–mass spectrometry (LC–MS) can analyze compounds with semi-polar and lower volatility in a wider mass range without derivatization [9,10,11]. Since each eluted metabolite produces multiple mass signals, such as fragments, adducts and isotope peaks, LC–MS data contain thousands of metabolomic features for complex samples. The pre-processing methods are needed to extract meaningful features for further statistical analysis

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