Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies
By Shiquan Sun, Jiaqiang Zhu, Xiang Zhou. Nature Methods, 2019
434 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a6c405c30b562bf779108d3b229d7556fad508c5
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In Molecular Biology
- Fabian J Theis
- Aviv Regev
- Ying Ma
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- David S. Fischer
- Xiang Zhou
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In Statistics and Probability
Citations in the corpus
- Spatial components of molecular tissue biology (2022, positive): “A recent comprehensive power analysis, using synthetic datasets 165 , evaluated several meth-ods that rely on different test statistics 162–165 .”
- Best practices for single-cell analysis across modalities (2023, neutral): “Our work represents an entry point”
- Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, methodology): “Exploring the use of other kernels, such as the periodic kernels 74 , or incorporating histological image information, such as image intensity level, as additional coordinates 6,75 , which can be readily done in CARD, may capture diverse and rich spatial correlation patterns in the future.”