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Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies.
Metadata
Journalnature methods30.822Date
2020 Jan 27
7 months ago
Publication Type
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Journal Article
Volume
2020-02 / 17 : 193-200
Author
Sun S 1, 2, Zhu J 2, Zhou X 3, 4
Affiliation
  • 2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
  • 3. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. [email protected]
  • 4. Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. [email protected]
Doi
PMIDMESH
Algorithms
Gene Expression Regulation
Humans
Likelihood Functions
Transcriptome
Abstract
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.
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30.8
Nat Methodsnature methods
Metadata
LocationUnited States
FromNATURE PUBLISHING GROUP

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