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

TL;DR

This paper introduces an adversarial learning method to effectively integrate unpaired single-cell chromatin accessibility and gene expression data, improving upon existing approaches in accuracy and robustness.

Contribution

The novel semi-supervised adversarial approach enhances integration of multi-modal single-cell data, outperforming current state-of-the-art methods.

Findings

Significantly improves data integration quality.

Outperforms two leading existing methods.

Effective in semi-supervised settings.

Abstract

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.