Statistical Principles Define an Open-Source Differential Analysis Workflow for Mass Spectrometry Imaging Experiments...

Area of Science:

• Biomedical imaging
• Analytical chemistry
• Computational biology

Background:

• Mass spectrometry imaging (MSI) reveals molecular spatial distributions in biological tissues.
• Complex experimental designs in MSI are crucial for understanding differential analyte abundance.
• Rigorous statistical analysis is essential for accurate interpretation of complex MSI data.

Purpose of the Study:

• To develop and present a comprehensive statistical analysis workflow for MSI experiments with complex designs.
• To illustrate the impact of key analytical decisions on the detection of differentially abundant analytes.
• To provide an open-source implementation for reproducible research in MSI data analysis.

Main Methods:

• Development of a statistical workflow incorporating signal processing and feature aggregation.
• Application of the workflow to histologic samples of human tibial plateaus (osteoarthritis patients vs. controls) and simulated datasets.
• Comparison of various statistical models for differential analysis, emphasizing the role of replication and sample size calculation.

Main Results:

• Signal processing and feature aggregation are critical for preserving biological relevance and managing multiple testing.
• Region of interest selection must be compatible with differential analysis methods.
• The study demonstrates the effectiveness of different statistical models and highlights the importance of replication.

Conclusions:

• The proposed statistical workflow enhances the analysis of complex MSI experiments.
• The findings emphasize the importance of careful data processing, region selection, and appropriate statistical modeling.
• An open-source R implementation is provided to facilitate the adoption of this workflow in future MSI studies.