The msFeaST pipeline requires spectral data, feature quantification tables, and statistical group indicator data for samples to work. When working with feature based molecular networking data, preprocessing functionalities are available to load the data accordingly . Once the data are loaded, the msFeaST pipeline method is initialized and handles all intermediate data structures. The workflow guides the user through the data processing steps in the form of spectral similarity computations using the desired similarity score, k-medoid clustering based on the resulting distance matrix, and t-SNE embedding using the same structure. We refer to supplementary sections 1.1 to 1.4 for further details. Here, we will focus specifically on the statistical testing and interactive visualization components.