Figure 2.

The architecture of GraphSCI model

The input of GraphSCI is the gene expression matrix and the gene-to-gene relationships. The Inference model ${\mathit{f}}_{\phi }$ is to learn the low-dimensional representations of genes and cells based on a combination of graph convolution network and Autoencoder neural network. The Generative model ${\mathit{g}}_{\mathit{\varphi }}$ utilizes the posterior distributions to reconstruct gene expression and gene-to-gene relationships respectively.