SPARK(编程语言)
转录组
空间分析
计算生物学
计算机科学
数据挖掘
可扩展性
统计能力
统计假设检验
统计模型
空间生态学
I类和II类错误
生物
基因
人工智能
基因表达
统计
数学
遗传学
生态学
数据库
程序设计语言
作者
Shuang Sun,Jiaqiang Zhu,Xiang Zhou
出处
期刊:Nature Methods
[Springer Nature]
日期:2020-01-27
卷期号:17 (2): 193-200
被引量:276
标识
DOI:10.1038/s41592-019-0701-7
摘要
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.
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