# Graph Neural Networks for Multimodal Single-Cell Data Integration

arXiv: https://arxiv.org/abs/2203.01884 · HTML: https://mlsanity.com/paper/2203.01884/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [A Unified View on Graph Neural Networks as Graph Signal Denoising](https://mlsanity.com/paper/2010.01777/) (2020)

## Built on by (most cited first, 18 of 18)

- [Condensing Graphs via One-Step Gradient Matching](https://mlsanity.com/paper/2206.07746/) (2022)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Learning on Multimodal Graphs: A Survey](https://mlsanity.com/paper/2402.05322/) (2024)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective](https://mlsanity.com/paper/2206.07743/) (2022)
- [Test-Time Training for Graph Neural Networks](https://mlsanity.com/paper/2210.08813/) (2022)
- [Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective](https://mlsanity.com/paper/2402.08228/) (2024)
- [A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data](https://mlsanity.com/paper/2303.06595/) (2023)
- [Theoretical guarantees for the advantage of GNNs over NNs in generalizing bandlimited functions on Euclidean cubes](https://mlsanity.com/paper/2206.05904/) (2022)
- [Deep Learning in Single-Cell Analysis](https://mlsanity.com/paper/2210.12385/) (2022)
- [Single-Cell Multimodal Prediction via Transformers](https://mlsanity.com/paper/2303.00233/) (2023)
- [Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters](https://mlsanity.com/paper/2311.17103/) (2023)
- [Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors](https://mlsanity.com/paper/2508.06257/) (2025)
- [Sub-graph Based Diffusion Model for Link Prediction](https://mlsanity.com/paper/2409.08487/) (2024)
- [ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data](https://mlsanity.com/paper/2505.12638/) (2025)
- [MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder](https://mlsanity.com/paper/2510.06880/) (2025)
- [DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis](https://mlsanity.com/paper/2602.01839/) (2026)

## Abstract (arXiv)

Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics. However, it is challenging to learn the joint representations from the multimodal data, model the relationship between modalities, and, more importantly, incorporate the vast amount of single-modality datasets into the downstream analyses. To address these challenges and correspondingly facilitate multimodal single-cell data analyses, three key tasks have been introduced: textitmodality prediction, textitmodality matching and textitjoint embedding. In this work, we present a general Graph Neural Network framework textitscMoGNN to tackle these three tasks and show that textitscMoGNN demonstrates superior results in all three tasks compared with the state-of-the-art and conventional approaches. Our method is an official winner in the overall ranking of textitModality prediction from NeurIPS 2021 Competition, and all implementations of our methods have been integrated into DANCE package~urlhttps://github.com/OmicsML/dance.