ABSTRACT We have entered the multi-omics era, and we can measure cells from different aspects. When dealing with such multi-omics data, the first step is to determine the correspondence among different omics. In other words, we should match data from different spaces corresponding to the same object. This problem is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Secondly, matched single-cell multi-omics data are rare and hard to collect. Furthermore, due to the limitations of the experimental environment, the data are usually highly noisy. To promote the single-cell multi-omics research, we overcome the above challenges, proposing a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward. Compared with the other state-of-the-art methods, our method performs better on both simulated and real single-cell data. On the real data, the performance improvement on accuracy over the previous methods is up to 55.7% regarding scRNA-seq and scATAC-seq data integration. Downstream trajectory inference analysis shows that our tool can transfer the labels from scRNA-seq to scATAC-seq with very high accuracy, which indicates our method’s effectiveness.

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration