JASPER: Joint Bayesian Analysis of Spatial Expression via Regression

TL;DR

JASPER is a Bayesian framework that jointly models spatial gene expression patterns to improve detection accuracy and biological interpretability in spatial transcriptomics data.

Contribution

It introduces a joint Bayesian approach with spatial basis functions, addressing limitations of existing methods that ignore gene correlations and rely on fixed kernels.

Findings

JASPER outperforms existing methods in real spatial transcriptomic datasets.

It identifies genes with stronger spatial correlation and biological relevance.

JASPER enhances statistical and biological interpretability of spatial gene expression.

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.