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

Ye Yuan,Ziv Bar-Joseph

doi:10.1186/s13059-020-02214-w

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.

Highlights

Several computational methods have been developed over the last two decades to infer interaction between genes based on their expression [1]
Graph Convolutional Neural networks for Genes (GCNG) greatly improves upon correlation-based methods when trying to infer both autocrine and extracellular gene interactions involved in cell-cell interactions
The GCNG framework We extended ideas from graph convolutional neural networks (GCNs) [18, 19] and developed the Graph Convolutional Neural networks for Genes (GCNG), a general supervised computational framework for inferring gene interactions involved in cell-cell communication from spatial single cell expression data

Summary

Introduction

Several computational methods have been developed over the last two decades to infer interaction between genes based on their expression [1]. 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. 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]. Recently developed methods for spatial transcriptomics are providing high-throughput information about both, the expression of genes within a single cell and the spatial relationships between cells

Methods

Results

Conclusion