The second workflow was used for the profile dataset (i.e. biological tissues measured via a ZenoTOF instrument). Here a combination of feature detection (i.e., SAFD), componentization (i.e., CompCreate), alignment, clean-up, and hierarchical clustering analysis was used to show the difference between liver and brain tissues from the Chinook fish (Figure ). First, feature detection with SAFD (Section S1.2) was performed on the liver, brain, and blank samples, using 10000 iterations, 100 scans maximum peak width in the time domain, 20000 resolution, 0.02 minimum m/z window, 0.8 correlation threshold, 150 minimum intensity, 5% signal increment threshold, signal to background ratio of 2, and 1 second of minimum peak width in the time domain. Second, CompCreate (Section S1.3) was performed, using a 0.8 correlation threshold, 0.5 retention window percentage, 0.8 mass window percentage, and 50 minimum intensity. Next, both the feature and component lists were aligned using the same principle (Section S1.6). In other words the liver and brain files were aligned separately with their corresponding blank files, using a m/z tolerance of 0.005 Da and a time tolerance of 0.1 minutes. This allowed to use the blank filtering functions (Section S1.7) to filter the matrix specific blank features from the liver and brain samples, respectively. For this, the mean blank signal was used with a signal to blank ratio of 5. Then, based on the blank filtering information, the individual feature and component lists were filtered. These filtered lists were then used for the second alignment where the liver and brain samples were combined, using again a m/z tolerance of 0.005 Da and a time tolerance of 0.1 minutes. This resulted in a aligned feature file and a aligned component file of all the samples (i.e., liver and brain). Finally, hierarchical cluster analysis was performed on the two aligned lists.