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Area of Science:

  • Biomedical Imaging
  • Analytical Chemistry
  • Data Science

Background:

  • Mass spectrometry imaging (MSI) generates spatially resolved molecular data.
  • Technical variations, termed "batch effects," cause ion intensity fluctuations unrelated to sample composition.
  • These batch effects limit MSI's application in large-scale clinical and preclinical research.

Purpose of the Study:

  • To address the lack of standardized batch correction methods in MSI.
  • To evaluate the effectiveness of batch correction strategies for spatially resolved MSI data.
  • To assess the impact of correction on intensity variability and spatial information.

Main Methods:

  • Developed and applied pixel-by-pixel batch correction techniques to MSI datasets.
  • Assessed the stabilization of ion intensity fluctuations across different batches.
  • Evaluated the preservation of expected spatial intensity variations within samples.

Main Results:

  • Demonstrated that batch correction can reduce technical variability in MSI data.
  • Showcased the importance of considering spatial data characteristics during correction.
  • Provided a framework for evaluating the utility of correction methods for MSI.

Conclusions:

  • Batch correction is crucial for advancing MSI in large-scale studies.
  • Pixel-by-pixel evaluation offers a robust approach to assess MSI batch correction.
  • This work contributes to the development of reliable MSI data analysis pipelines.