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

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 Convol...

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Bibliographic Details
Published inGenome Biology Vol. 21; no. 1; p. 300
Main Authors Yuan, Ye, Bar-Joseph, Ziv
Format Journal Article
LanguageEnglish
Published England BioMed Central 10.12.2020
BMC
Subjects
Animals
Computational Biology - methods
Datasets
Epistasis, Genetic
Extracellular gene interactions
Gene Expression
Graph convolutional networks
Humans
Ligands
Method
Methods
Mice
Neighborhoods
Neural networks
Neural Networks, Computer
Proteins
Software
Spatial transcriptomics
Transcriptome
Spatial transcriptomics
Graph convolutional networks
Extracellular gene interactions
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Summary: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 .
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-020-02214-w