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Cited by 77Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksAdvanced Graph Neural Networks

Graph Neural Networks for Multimodal Single-Cell Data Integration

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Wen, Hongzhi, Ding, Jiayuan, Jin, Wei, Wang, Yiqi, Xie, Yuying, Tang, Jiliang·2022·Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
DOI 10.1145/3534678.3539213
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Abstract

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: Modality prediction, Modality matching andJoint embedding. In this work, we present a general Graph Neural Network framework scMoGNN to tackle these three tasks and show that scMoGNN 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 ofModality prediction from NeurIPS 2021 Competition (https://openproblems.bio/neurips_2021/), and all implementations of our methods have been integrated into DANCE package (https://github.com/OmicsML/dance).

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Cited by

Sorted by citations

Authors
Wen, H., Ding, J., Jin, W., Wang, Y., Xie, Y., & Tang, J.
Affiliations
Michigan State University (US)
ORCID
Venue
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher
ACM
Published
2022-08-14
Pages
4153-4163
DOI
10.1145/3534678.3539213
OpenAlex
W4226218349
Cited by
77
References
2
Citations by year
2026: 12 · 2025: 17 · 2024: 32 · 2023: 12 · 2022: 4
FWCI
75.63
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Science Foundation (S1714741, CNS1815636, IIS1845081, IIS1907704, IIS1928278, IIS1955285, IOS2107215, IOS2035472)
  • Army Research Office (W911NF-21-1-0198)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Keywords
graph neural networks, multimodal single-cell data, data integration, joint embedding, multi-omics, dance

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