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

Tideman, L. E. M.; Moser, F. A.; Migas, L. G.; Spathies, J.; Djambazova, K. V.; Marshall, C. R.; Schrag, M. S.; Skaar, E. P.; Spraggins, J. M.; Van de Plas, R.

2025-10-28 bioinformatics
10.1101/2025.10.27.684518 bioRxiv
Show abstract

Multiplexed molecular imaging, such as imaging mass spectrometry, provides spatially-contextualized insights that are transforming biomedical research by advancing our understanding of tissue organization and disease mechanisms. However, the spatial-statistical properties of molecular imaging data are often underutilized, and scalable computational tools to analyze them are lacking. Here, we demonstrate how local and global spatial autocorrelation (SAC) metrics can be used to quantify spatial dependence and spatial heterogeneity, properties that violate the common assumption of independent and identically distributed measurements. We develop mathematically rigorous methods for SAC-based exploratory analysis. Furthermore, we introduce a novel spatial feature extractor, the Moran quadrant map, and develop two advanced workflows based on it: Moran-Felsenszwalb segmentation for tissue domain segmentation and Moran-HOG clustering for colocalization-based image analysis. Finally, our open-source Moran Imaging toolbox (https://github.com/vandeplaslab/Moran_Imaging) provides scalable Python implementations, including a novel parallelized spatial lag algorithm, unlocking SAC-based analyses and biological insights for large-scale imaging.

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