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GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data.

ABSTRACT: 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 .

SUBMITTER: Yuan Y

PROVIDER: S-EPMC7726911 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data.

Yuan Ye Y Bar-Joseph Ziv Z

Genome biology 20201210 1

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 analy ...[more]

PMID: 33303016

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