Evaluating Batch Correction Methods for Large-Scale Mass Spectrometry Imaging of Heterogeneous Tissues

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.