DEIMoS: an open-source tool for processing high-dimensional mass spectrometry data

TL;DR

DEIMoS is an open-source Python tool that enhances high-dimensional mass spectrometry data analysis by providing efficient algorithms for feature detection, alignment, and spectral deconvolution across multiple dimensions.

Contribution

It introduces a versatile, multidimensional approach to mass spectrometry data processing, improving detection sensitivity and data alignment in a largely instrument-agnostic manner.

Findings

Enhanced feature detection sensitivity

Improved dataset alignment confidence

Effective mitigation of convolution artifacts

Abstract

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data to illustrate the advantages of a multidimensional approach in each data processing step.