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We characterized noise in Orbitrap mass spectrometry, finding it varies with signal intensity. A new method, WSoR, effectively reduces noise bias in multivariate analysis for better life-science data interpretation.

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Area of Science:

  • Analytical Chemistry
  • Mass Spectrometry
  • Life Sciences

Background:

  • Orbitrap mass spectrometry is a vital tool in life sciences.
  • Non-uniform noise in mass spectrometers introduces bias, complicating data interpretation.
  • Understanding noise structure is crucial for accurate analysis.

Purpose of the Study:

  • To investigate the noise structure of an Orbitrap mass analyzer in a secondary ion mass spectrometer (OrbiSIMS).
  • To develop a generative model and scaling method (WSoR) to address noise bias in Orbitrap data.
  • To evaluate WSoR's effectiveness in multivariate analysis of biological imaging data.

Main Methods:

  • Characterization of noise regimes in OrbiSIMS Orbitrap based on signal intensity.
  • Development of a generative model accounting for Orbitrap data noise distribution.
  • Implementation and comparison of the WSoR scaling method against existing techniques.

Main Results:

  • Identified three distinct noise regimes in Orbitrap analysis: detector/censoring noise (low signal), counting noise (intermediate signal), and measurement variation (high signal).
  • WSoR demonstrated superior performance in discriminating chemical information from noise across diverse biological datasets.
  • Existing scaling methods showed variable performance, highlighting the need for robust noise-handling techniques.

Conclusions:

  • The developed generative model and WSoR scaling method effectively address noise bias in Orbitrap mass spectrometry.
  • WSoR offers a consistent and improved approach for multivariate analysis of complex biological imaging data.
  • Accurate noise modeling is essential for reliable data interpretation in Orbitrap-based life science applications.