Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies

• Published in: Nature methods Vol. 17; no. 2; pp. 193 - 200
• Main Authors: Sun, Shiquan, Zhu, Jiaqiang, Zhou, Xiang
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.02.2020; Nature Publishing Group
• Subjects: 631/114/2415; 631/114/794; 631/1647/2217/2018; 631/61/212/2019; Algorithms; Analysis; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Datasets; Gene expression; Gene Expression Regulation; Genes; Genetic research; Humans; Life Sciences; Likelihood Functions; Proteomics; Spatial analysis; Spatial data; Statistical analysis; Statistical methods; Statistics; Transcriptome
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-019-0701-7

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches. A statistical method called SPARK for analyzing spatially resolved transcriptomic data can efficiently identify spatially expressed genes with effective control of type I errors and high statistical power.