Hao Minsheng, Hua Kui, Zhang Xuegong
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.
School of Life Sciences, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China.
Bioinformatics. 2021 Dec 7;37(23):4392-4398. doi: 10.1093/bioinformatics/btab471.
Recent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue microenvironments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.
We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses self-organizing map to cluster neighboring cells into nodes, and then uses a Gaussian process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ∼5 min in large datasets of more than 20 000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde free for academic use.
SOMDE is available for download from PyPI, and the source code is openly available from the Github repository https://github.com/XuegongLab/somde.
Supplementary data are available at Bioinformatics online.
空间转录组测序技术的最新发展为在组织微环境的物理背景下理解细胞提供了强大工具。空间基因表达分析中的一项基本任务是识别具有空间可变表达模式的基因，即空间可变基因（SV基因）。已经针对此任务开发了几种计算方法。它们的高计算复杂性限制了它们对最新和未来大规模空间表达数据的扩展性。
我们提出了SOMDE，一种在大规模空间表达数据中识别SV基因的有效方法。SOMDE使用自组织映射将相邻细胞聚类成节点，然后使用高斯过程拟合节点级空间基因表达以识别SV基因。实验表明，SOMDE比现有方法快约5至50倍，且结果相当。SOMDE的可调分辨率使其成为唯一一种能在超过20000个测序位点的大型数据集中在约5分钟内给出结果的方法。SOMDE作为一个Python包可在PyPI上获取，网址为https://pypi.org/project/somde，供学术免费使用。
SOMDE可从PyPI下载，其源代码可从Github仓库https://github.com/XuegongLab/somde公开获取。
补充数据可在《生物信息学》在线获取。
