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Basics of Multivariate Analysis in Neuroimaging Data

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Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical

Léonore E M Tideman¹, Felipe A Moser¹, Lukasz G Migas¹

¹Delft Center for Systems and Control, Delft University of Technology, Delft 2628 CD, Netherlands.

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

Spatial autocorrelation (SAC) metrics reveal spatial patterns in molecular imaging data, overcoming limitations of traditional analysis. This work introduces novel tools for enhanced tissue analysis and disease mechanism discovery.

Keywords:

colocalization image clustering imaging mass spectrometry machine learning multiplexed molecular imaging semantic image segmentation spatial autocorrelation spatial data analysis spatial dependence spatial filtering spatial heterogeneity spatial omics

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Area of Science:

Biomedical research
Computational biology
Spatial statistics

Background:

Multiplexed molecular imaging, like imaging mass spectrometry, offers spatially-contextualized data crucial for understanding tissue organization and disease.
Current analysis often underutilizes spatial-statistical properties, lacking scalable computational tools.
Standard assumptions of independent measurements are frequently violated in imaging data.

Purpose of the Study:

To quantify spatial dependence and heterogeneity in molecular imaging data using spatial autocorrelation (SAC) metrics.
To develop novel computational tools for analyzing these spatial properties.
To enable advanced biological insights from large-scale imaging datasets.

Main Methods:

Application of local and global spatial autocorrelation (SAC) metrics.
Development of mathematically rigorous methods for SAC-based exploratory analysis.
Introduction of the Moran quadrant map for spatial feature extraction.
Creation of Moran-Felsenszwalb segmentation and Moran-HOG clustering workflows.
Implementation of a parallelized spatial lag algorithm within the open-source Moran Imaging toolbox.

Main Results:

Demonstration of SAC metrics for quantifying spatial dependence and heterogeneity.
Successful development of novel spatial analysis workflows for tissue segmentation and colocalization.
Provision of scalable Python implementations for advanced imaging analysis.
Unlocking new biological insights through SAC-based analysis of large-scale imaging data.

Conclusions:

Spatial autocorrelation metrics provide powerful tools for analyzing molecular imaging data.
The developed Moran Imaging toolbox offers scalable solutions for underutilized spatial properties.
This approach advances our understanding of tissue organization and disease mechanisms through enhanced imaging analysis.