December 6, 2025Journal of ClassificationOpen Access

Spatially Regularized Gaussian Mixtures for Clustering Spatial Transcriptomic Data

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Discussion

Proposed methods cluster gene expression patterns in tissue samples, indicating spatial variation in gene behavior.

Gene clusters with distinctive spatial correlation patterns were identified across tissue areas, highlighting their biological significance.
The analysis of spatial transcriptomics data leverages Gaussian processes for effective clustering of gene expression.
The modeling framework utilizes a generalized Cholesky decomposition to manage spatial covariance structures effectively.
This approach may prevent kernel misspecification, facilitating novel insights into gene behavior in distinct tissue environments.

Sottosanti et al. (2025) studied this question.

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