Orbitrap noise structure and method for noise-unbiased multivariate analysis

Abstract

Abstract Orbitrap mass spectrometry is widely used in the life-sciences. However, like all mass spectrometers, non-uniform (heteroscedastic) noise introduces bias in multivariate analysis complicating data interpretation. Here, we study the noise structure of a high-field Orbitrap mass analyzer integrated into a secondary ion mass spectrometer (OrbiSIMS). Using a stable primary ion beam to provide a well-controlled source of secondary ions from a silver sample, we find that noise has three characteristic regimes (1) at low signals the ion trap detector noise and a censoring algorithm dominate, (2) at intermediate signals counting noise specific to the SIMS emission process is most significant and has Poisson-like statistical properties, and (3) at high signal levels other sources of measurement variation become important and the data are overdispersed relative to Poisson. We developed a generative model for Orbitrap-based mass spectrometry data that directly incorporates the number of ions and accounts for the noise distribution over the entire intensity range. We find, for silver ions, a detection limit of 3.7 ions independent of ion generation rate. Using this understanding, we introduce a new scaling method, termed WSoR, to reduce the effects of noise bias in multivariate analysis and show it is more effective than the most common data preprocessing methods (root mean scaling, Pareto scaling and log transform) for the simple silver data. For more complex biological images with lower signal intensities the WSoR, Pareto and root mean scaling methods have similar performance and are significantly better than no scaling or, especially, log transform.