# Graph Neural Network approaches for single-cell data: A recent overview

arXiv: https://arxiv.org/abs/2310.09561 · HTML: https://mlsanity.com/paper/2310.09561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 94 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Gated Graph Recurrent Neural Networks](https://mlsanity.com/paper/2002.01038/) (2020)
- [A Survey of Adversarial Learning on Graphs](https://mlsanity.com/paper/2003.05730/) (2020)

## Built on by (most cited first, 2 of 2)

- [LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification](https://mlsanity.com/paper/2408.17384/) (2024)
- [GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation](https://mlsanity.com/paper/2508.04747/) (2025)

## Abstract (arXiv)

Graph Neural Networks (GNN) are reshaping our understanding of biomedicine and diseases by revealing the deep connections among genes and cells. As both algorithmic and biomedical technologies have advanced significantly, we're entering a transformative phase of personalized medicine. While pioneering tools like Graph Attention Networks (GAT) and Graph Convolutional Neural Networks (Graph CNN) are advancing graph-based learning, the rise of single-cell sequencing techniques is reshaping our insights on cellular diversity and function. Numerous studies have combined GNNs with single-cell data, showing promising results. In this work, we highlight the GNN methodologies tailored for single-cell data over the recent years. We outline the diverse range of graph deep learning architectures that center on GAT methodologies. Furthermore, we underscore the several objectives of GNN strategies in single-cell data contexts, ranging from cell-type annotation, data integration and imputation, gene regulatory network reconstruction, clustering and many others. This review anticipates a future where GNNs become central to single-cell analysis efforts, particularly as vast omics datasets are continuously generated and the interconnectedness of cells and genes enhances our depth of knowledge in biomedicine.