GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
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GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data Ye Yuan1 and Ziv Bar-Joseph1,2* * Correspondence: [email protected]. edu 1 Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA 2 Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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. Keywords: Spatial transcriptomics, Graph convolutional networks, Extracellular gene interactions
Background Several computational methods have been developed over the last two decades to infer interaction between genes based on their expression [1]. Early work utilized large compendiums of microarray data [2] while more recent work focused on RNA-Seq and scRNA-Seq [3]. While the identification of pairwise interactions was the goal of several studies that relied on such methods, others used the results as features in a classification framework [4] or as pre-processing steps for the reconstruction of biological interaction networks [5]. Most work to date focused on intra-cellular interactions and network. In such studies, we are looking for interacting genes involved in a pathway or in the regulation of other genes within a specific cell. In contrast, studies of extracellular interactions (i.e., interactions of genes or proteins in different cells) mainly utilized small-scale experiments in which a number of ligand and receptor pairs were studied in the context of a cell line or tissue [6]. However, recently developed methods for spatial transcriptomics are now providing high-throughput information about both, the expression of genes within a single cell and the spatial relationships between cells © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the mater
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