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Deep learning has empowered analysis for single-cell sequencing data in manyways and has generated deep understanding about a range of complex cellularsystems. As the booming single-cell sequencing technologies brings the surge ofhigh dimensional data that come from different sources and represent cellularsystems with different features, there is an equivalent rise and challenge ofintegrating single-cell sequence across modalities. Here, we present a noveladversarial approach to integrate single-cell chromatin accessibility and geneexpression data in a semi-supervised manner. We demonstrate that our methodsubstantially improves data integration from a simple adversarial domainadaption approach, and it also outperforms two state-of-the-art (SOTA) methods.

Integration of Unpaired Single-cell Chromatin Accessibility and Gene Expression Data via Adversarial Learning