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

Ethan B T Rogers1, Sai Srikanth Lakkimsetty1, Kylie Ariel Bemis1

  • 1Khoury College of Computer Sciences, Northeastern University, Boston MA.

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Summary
This summary is machine-generated.

This study presents a statistical workflow for analyzing complex mass spectrometry imaging (MSI) data to find differential analyte abundance. The workflow improves data processing and statistical modeling for robust biological insights.

Keywords:
Complex DesignsData Analysis WorkflowDifferential AbundanceMass Spectrometry ImagingOsteoarthritisStatistical Inference

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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.