AI Insight
SpaceDX is a new computational framework that simultaneously identifies which genes show differential expression between conditions and determines where in tissue those differences occur, without requiring predefined spatial regions. In analyses of mouse brain datasets studying chronic stress and Alzheimer's disease, SpaceDX detected 19-46% more differentially expressed genes compared to traditional methods that rely on pre-specified tissue regions. The method also generates interpretable spatial maps showing where condition-associated gene expression changes are localized within tissues.
Why it matters
This approach could improve our understanding of spatially complex diseases by revealing gene expression changes that occur in specific tissue locations but might be missed by conventional analysis methods. The ability to discover both what genes are affected and where they are affected simultaneously, without prior knowledge of relevant tissue structures, could accelerate research in neuroscience, cancer biology, and other fields where spatial context is important.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Multi-subject spatial transcriptomics is increasingly used to identify molecular differences across individuals and conditions while resolving where in tissue those differences occur. Yet subject-level differential expression (DE) analyses commonly require spatial regions to be specified in advance, separating localization from testing. Here we present SpaceDX, a framework that uses gene-specific attention-based spatial weighting to jointly test differential expression and localize the spatial expression patterns contributing to these associations. In simulations, SpaceDX outperforms cluster-based DE methods when differential expression does not align with predefined spatial boundaries. Across two multi-subject mouse brain datasets, SpaceDX detected 45.8% more DE genes than regional DESeq2 pseudobulk in a chronic stress model, while in an Alzheimer’s disease model combining SpaceDX with DESeq2 increased discovery by 19.1% relative to DESeq2 alone. Across both datasets, the learned maps provided spatial context for condition-associated expression signals in addition to gene-level significance. In the chronic stress model, for example, oligodendrocyte- and myelin-associated genes showed shared localization to corresponding brain structures. Together, these results show that the spatial context of differential expression can be inferred jointly with gene-level associations, recovering signals obscured by regional aggregation without requiring the relevant tissue context to be defined in advance.
Source: Joint localization and testing reveals spatial context of differential expression