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- Title
Scalable integration of multiomic single-cell data using generative adversarial networks.
- Authors
Giansanti, Valentina; Giannese, Francesca; Botrugno, Oronza A; Gandolfi, Giorgia; Balestrieri, Chiara; Antoniotti, Marco; Tonon, Giovanni; Cittaro, Davide
- Abstract
Motivation Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable. Results We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. Availability and implementation Source code of our framework is available at https://github.com/vgiansanti/MOWGAN
- Subjects
GENERATIVE adversarial networks; DATA integration; SOURCE code; PROBABILISTIC generative models; PROTEOMICS; PAIRED comparisons (Mathematics)
- Publication
Bioinformatics, 2024, Vol 40, Issue 5, p1
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btae300