Fig. 1
From: GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
GCNG for extracellular gene relationship inference. a GCNG model using spatial single cell expression data. A binary cell adjacent matrix and an expression matrix are extracted from spatial data. After normalization, both matrices are fed into the graph convolutional network. b Training and test data separation and generation strategy. The known ligand and receptor genes can form complicated directed networks. For cross-validation, all ligand and receptors are separated exclusively as training and test gene sets, and only gene pairs where both genes are in training (test) gene set are used for training (test). To balance the dataset, each positive ligand-receptor (La, Rb) gene pair with label 1 will have a negative pair sample (La, Rx) with label 0 where Rx was randomly selected from all training (test) receptor genes which are not interacting with La in training (test)