ArticleBioinformatics (Oxford, England)2023
Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Integrative Omics and AI-Driven Systems Biology: Multilayer Networks DecodingJournal of proteome research · 2025Review
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.BMC genomics · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- sCIN: a contrastive learning framework for single-cell multi-omics data integration.Briefings in bioinformatics · 2025Article
- Recent advances in designing synthetic plant regulatory modules.Frontiers in plant science · 2025Review
- A deep learning adversarial autoencoder with dynamic batching displays high performance in denoising and ordering scRNA-seq data.iScience · 2024Article
- MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.BioData mining · 2024Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.bioRxiv : the preprint server for biology · 2024Article
- Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology.Biodesign research · 2024Review
- The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.Frontiers in molecular biosciences · 2022Review
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9 authors.
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Abstract
motivationWe have entered the multi-omics era and can measure cells from different aspects. Hence, we can get a more comprehensive view by integrating or matching data from different spaces corresponding to the same object. However, it is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Though some techniques can be used to measure scATAC-seq and scRNA-seq simultaneously, the data are usually highly noisy due to the limitations of the experimental environment.
resultsTo promote single-cell multi-omics research, we overcome the above challenges, proposing a novel framework, contrastive cycle adversarial autoencoders, which can align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Con-AAE can efficiently map the above data with high sparsity and noise from different spaces to a coordinated subspace, where alignment and integration tasks can be easier. We demonstrate its advantages on several datasets. AVAILABILITY AND IMPLEMENTATION: Zenodo link: https://zenodo.org/badge/latestdoi/368779433. github: https://github.com/kakarotcq/Con-AAE.
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