Scalable Integration of Multiomic Single-cell Data Using Generative Adversarial Networks

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2024 May 2

PMID 38696763

Authors

Valentina Giansanti

Francesca Giannese

Oronza A Botrugno

Giorgia Gandolfi

Chiara Balestrieri

Marco Antoniotti

Giovanni Tonon

Davide Cittaro

Affiliations

Soon will be listed here.

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.

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