SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

ABSTRACT: Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent

SUBMITTER: Vasighizaker A 

PROVIDER: S-EPMC11009470 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature