Protocol to analyze 1D and 2D mass spectrometry data from glioblastoma tissues for cancer diagnosis and immune cell identification

In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is crucial. Moreover, exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present a protocol to analyze 1D and 2D MS data from glioblastoma tissues for cancer diagnosis and immune cells identification. We describe steps for training the most optimal model and cross-validating it, for discovering robust biomarkers and obtaining their corresponding boxplots as well as creating an immunoscore based on MS-imaging data. For complete details on the use and execution of this protocol, please refer to Zirem et al.