Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL
Abstract
High-throughput single-cell sequencing is widely used to study cell identity. We present SEAGALL (Single-cell Explainable Geometry-Aware Graph Attention Learning pipeLine), a deep learning method to quantify the impact of molecular features on cellular phenotype, based on geometry-regularised autoencoders (GRAE) and explainable graph attention networks (X-GAT). The GRAE embeds the data into a latent space to build a reliable cell-cell graph. The GAT is trained to learn the annotations and XAI is used to explain the predictions, unravelling the features driving cell identity. SEAGALL extracts specific and stable signatures from multiple omics experiments, going beyond differential marker genes.
Fields of Study & Subjects
Single-cell and spatial transcriptomics
Cell Image Analysis Techniques
Bioinformatics and Genomic Networks
Publication details
- Journal / source
- Genome biology
- Publisher
- Springer Science and Business Media LLC
- Volume
- 27
- Issue
- 1
- Publication date
- 23 April 2026
- Language
- EN