JASPER: Joint Bayesian Analysis of Spatial Expression via Regression

2026-04-20

AI Summary

Existing spatial transcriptomics analysis methods often overlook inter-gene correlations and rely on predefined covariance kernels, which can lead to inflated false positives and false negatives. To address this limitation, this work proposes JASPER—a Bayesian joint modeling framework that, for the first time in spatial transcriptomics, simultaneously captures multi-gene expression patterns through spatial basis function regression without assuming a prespecified covariance structure. By avoiding rigid parametric assumptions about spatial dependence, JASPER substantially enhances statistical robustness and biological interpretability. Extensive evaluation on both real and simulated datasets demonstrates that JASPER consistently identifies gene modules with stronger spatial coherence and clearer functional relevance, as corroborated by pathway and enrichment analyses.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Kernel Methods
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Spatially resolved transcriptomics is a fast-developing set of technologies that enables the measurement of localized gene expression across spatial locations in a sample. Detecting spatially varying genes is critical for analyzing such data, yet existing methods often fail to account for inter-gene correlations, leading to inflated false positive and false negative rates. Additionally, most prominent methods rely on predefined spatial covariance kernels, making them sensitive to the complexity of spatial expression patterns. Motivated by a human breast cancer dataset, we address these limitations in existing literature through JASPER (Joint Bayesian Analysis of SPatial Expression via Regression), a Bayesian framework that jointly models spatial expression patterns across multiple genes using a spatial basis function regression approach. We demonstrate the superior performance of JASPER compared to existing methods in several real-world spatial transcriptomic datasets and supporting simulation experiments. JASPER identifies genes with stronger spatial correlation and greater biological relevance, as validated by overlap comparison, enrichment analysis, and pathway analysis using independent biological databases. Our results highlight the ability of JASPER to improve the statistical and biological interpretability of spatial transcriptomics data, making it a powerful tool for uncovering spatial gene expression patterns in complex biological systems.

Problem

• spatially resolved transcriptomics
• spatially varying genes
• inter-gene correlations
• spatial covariance kernels
• false positive rate

Innovation

• spatially resolved transcriptomics
• Bayesian modeling
• spatial basis function regression
• inter-gene correlation
• spatially varying genes