A learned embedding for efficient joint analysis of millions of mass spectra

[evancofer]

Despite an explosion of data in public repositories, peptide mass spectra are usually analyzed by each laboratory in isolation, treating each experiment as if it has no relationship to any others. This approach fails to exploit the wealth of existing, previously analyzed mass spectrometry data. Others have jointly analyzed many mass spectra, often using clustering. However, mass spectra are not necessarily best summarized as clusters, and although new spectra can be added to existing clusters, clustering methods previously applied to mass spectra do not allow new clusters to be defined without completely re-clustering. As an alternative, we propose to train a deep neural network, called "GLEAMS," to learn an embedding of spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We demonstrate empirically the utility of this learned embedding by propagating annotations from labeled to unlabeled spectra. We further use GLEAMS to detect groups of unidentified, proximal spectra representing the same peptide, and we show how to use these spectral communities to reveal misidentified spectra and to characterize frequently observed but consistently unidentified molecular species. We provide a software implementation of our approach, along with a tool to quickly embed additional spectra using a pre-trained model, to facilitate large-scale analyses.

https://doi.org/10.1101/483263