基于主成分分析的空间域识别，具有一流的性能。
PCA-based spatial domain identification with state-of-the-art performance.
作者信息
Schaub Darius P, Yousefi Behnam, Kaiser Nico, Khatri Robin, Puelles Victor G, Krebs Christian F, Panzer Ulf, Bonn Stefan
机构信息
Institute of Medical Systems Bioinformatics, Center for Biomedical AI (bAIome), Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany.
III Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany.
出版信息
Bioinformatics. 2024 Dec 26;41(1). doi: 10.1093/bioinformatics/btaf005.
MOTIVATION
The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.
RESULTS
Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.
AVAILABILITY AND IMPLEMENTATION
The code is available at https://github.com/imsb-uke/nichepca.
动机
识别具有生物学意义的区域是空间转录组数据分析的核心步骤。
结果
遵循奥卡姆剃刀原则，我们表明一种基于主成分分析（PCA）的简单无监督空间区域识别算法，在六个单细胞空间转录组数据集上可与十种竞争的最先进方法相媲美。我们的简化方法NichePCA为研究人员提供了直观的区域解释，并且在执行速度、稳健性和可扩展性方面表现出色。
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