  Deep learning has empowered analysis for single-cell sequencing data in many
ways and has generated deep understanding about a range of complex cellular
systems. As the booming single-cell sequencing technologies brings the surge of
high dimensional data that come from different sources and represent cellular
systems with different features, there is an equivalent rise and challenge of
integrating single-cell sequence across modalities. Here, we present a novel
adversarial approach to integrate single-cell chromatin accessibility and gene
expression data in a semi-supervised manner. We demonstrate that our method
substantially improves data integration from a simple adversarial domain
adaption approach, and it also outperforms two state-of-the-art (SOTA) methods.
