OP-07 sciCAN: Single-cell chromatin accessibility and gene expression data integration via Cycle-consistent Adversarial Network
Presenting Author: Yang Xu, The University of Tennessee, Knoxville
Abstract:As the booming single-cell sequencing technologies bring a surge of high dimensional data that come from different sources and represent cellular systems with different features, there is an equivalent increase in the challenges of integrating single-cell sequencing data across modalities. Here, we present a novel adversarial approach (sciCAN) to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 3 state-of-the-art (SOTA) methods in 5 different ATAC-seq/RNA-seq datasets, and we demonstrated that sciCAN dealt with data integration with better balance of mutual transferring between modalities than the other 3 SOTA methods. sciCAN, along with Seurat, has the best integration performance. Next, we applied sciCAN to both PBMC RNA-seq and ATAC-seq data and showed that the integrated representation learned sciCAN preserved HSC-centered hematopoiesis hierarchy in both modalities. Finally, we used sciCAN to jointly cluster single-cell CRISPR-screed K562 RNA-seq and ATAC-seq data, and we identified a subcluster enriching similar markers in both modalities, suggesting a common effect after CRISPR perturbation.
