Variance Analysis of LC-MS Experimental Factors and Their Impact on Machine Learning

Background Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data processing pipeline from raw data analysis to end-user predictions and re-scoring. ML models need large-scale datasets for training and re-purposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs. Results We aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variance in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning. Conclusions Our findings show significantly higher levels of homogeneity within a project than between projects, which indicates that it’s important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pre-trained model. ### Competing Interest Statement The authors have declared no competing interest. * ML : Machine Learning DL : Deep Learning MS : Mass Spectrometry LC-MS or MS1 : Liquid Chromatography-Mass Spectrometry LC-MS/MS, MS/MS or MS2 : Tandem mass-spectrometry m/z : Mass to charge ratio NCE : Normalized Collision Energy PTM : Post-translational modification CID : Collision induced dissociation HCD : high-energy C-trap dissociation ETD : electron-transfer dissociation ETciD : electron-transfer and collision-induced dissociation EThcD : electron-transfer and higher-energy collision dissociation PX : ProteomeXchange