Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

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.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

“…Metabolomics analysis can be performed using GC-MS and LC-MS, and LC-MS is commonly used for the analysis of lipidomics. The combination of metabolomics and AI has flourished in various areas of cancer, including breast cancer [165,166], head and neck cancer [167], colorectal cancer [168,169], glioma cancer [170], esophageal cancer [171,172], lung cancer [52,173], kidney cancer [174], and neuroendocrine tumors [175]. With the greatest prediction accuracy (AUC = 0.93) and a deeper understanding of disease biology, a DL technique has been shown to be beneficial for metabolomics-based breast cancer ER status categorization [176].…”