Protocol to analyse 1D and 2D mass spectrometry data for cancer diagnosis and immune cell identification

In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is a crucial point. Moreover, the exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present an artificial intelligence (AI) pipeline based on MS fingerprints for providing an accurate and reliable classification model and ensuring interpretability by discovering confident biomarkers. We also explored the possibility to create an immunoscore based on MS-imaging data without necessitating techniques reliant on probe use. For complete details on the use and execution of this protocol, please refer to Zirem et al.