LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening

Current preprocessing workflows for untargeted metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) are time-consuming and require significant domain knowledge. Furthermore, they lack reproducibility or may fail to detect some metabolites entirely. We introduce LCMS-Net, an end-to-end deep learning model for the analysis of LC-HRMS data, to address these challenges. LCMS-Net mitigates the need for manual data preprocessing by operating directly on the raw LC-HRMS data and explicitly modeling its spatial properties. The effectiveness of this fully automated workflow is shown through two case-studies, cause-of-death (CoD) screening and colon cancer detection. For the cause-of-death screening task, LCMS-Net achieved a 9% improvement in F1-score compared to the previous state-of-the-art model (OPLS-DA). For the colon cancer detection task, LCMS-Net achieved an F1-score improvement of 1.8% compared to the previous state-of-the-art model (DeepMSProfiler). Furthermore, LCMS-Net significantly reduces batch effects that are a common source of bias in metabolomics data analyses. This was shown by using a training and test set from different measurement instruments, where the performance only differed by at most 3% as to using data from the same instrument. Compared to other end-to-end deep learning methods for LC-HRMS data, LCMS-Net is also structurally simpler and does not rely on pretraining, which makes it faster and computationally more efficient.