GCNG：基于图卷积网络从空间转录组学数据推断基因互作。
GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data.
机构信息
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
出版信息
Genome Biol. 2020 Dec 10;21(1):300. doi: 10.1186/s13059-020-02214-w.
Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG). GCNG encodes the spatial information as a graph and combines it with expression data using supervised training. GCNG improves upon prior methods used to analyze spatial transcriptomics data and can propose novel pairs of extracellular interacting genes. The output of GCNG can also be used for downstream analysis including functional gene assignment.Supporting website with software and data: https://github.com/xiaoyeye/GCNG .
大多数从表达数据推断基因-基因相互作用的方法都侧重于细胞内相互作用。高通量空间表达数据的可用性为可以推断细胞内和细胞间相互作用的方法打开了大门。为此，我们开发了用于基因的图卷积神经网络（Graph Convolutional Neural networks for Genes，GCNG）。GCNG 将空间信息编码为图，并使用监督训练将其与表达数据相结合。GCNG 改进了先前用于分析空间转录组学数据的方法，并且可以提出新的细胞外相互作用基因对。GCNG 的输出也可用于包括功能基因分配在内的下游分析。支持软件和数据的网站：https://github.com/xiaoyeye/GCNG。
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