SpatialCorr identifies gene sets with spatially varying correlation structure

• Published in: Cell reports methods 2022-12, Vol.2 (12), p.100369, Article 100369
• Main Authors: Bernstein, Matthew N., Ni, Zijian, Prasad, Aman, Brown, Jared, Mohanty, Chitrasen, Stewart, Ron, Newton, Michael A., Kendziorski, Christina
• Format: Article
• Language: English
• Subjects: Carcinoma, Squamous Cell - genetics; differential correlation; Gene Expression Profiling - methods; Humans; Skin Neoplasms - genetics; spatial transcriptomics; statistical test; Transcriptome - genetics
• ISSN: 2667-2375; 2667-2375
• DOI: 10.1016/j.crmeth.2022.100369

Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. To this end, we present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. An application to cutaneous squamous cell carcinoma demonstrates the power of the approach for revealing biological insights not identified using existing methods. [Display omitted] •SpatialCorr detects spatially varying correlation between genes across space•SpatialCorr tests for changes in correlation both within and between tissue regions•SpatialCorr tests for changes in correlation among sets of genes in addition to pairs A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust statistical methods exist to address this challenge. While useful, these methods do not detect spatial changes in the coordinated expression within a group of genes. We present SpatialCorr, a method for identifying sets of genes with spatially varying correlation structure. Given a collection of gene sets pre-defined by a user, SpatialCorr tests for spatially induced differences in the correlation of each gene set within tissue regions, as well as between and among regions. Bernstein et al. present SpatialCorr, a statistical method for finding groups of genes whose interactions change across space in tissue samples measured by spatially resolved transcriptomics technologies. SpatialCorr detects genes whose interactions change both within tissue regions as well as between regions.