scKADE: Single-Cell Multi-Omics Integration with Kolmogorov-Arnold Deep Embedding

Single-cell sequencing technologies allow for a high-resolution examination of various biological activities, such as the genome, transcriptome, and epigenetic modifications, across thousands of cells. In contrast, single-omics approaches fall short in fully capturing the intricate molecular mechanisms. In this regrad, integrating multi-omics data can potentially provide a more comprehensive understanding of fundamental biological processes. Although sequencing technologies have emerged that can simultaneously obtain different omics from single cells, they are limited by throughput and the types of omics. A more common scenario is that data from different omics are measured independently, resulting in unpaired cell compositions. Therefore, efficient algorithms are needed to integrate and analyze unpaired multi-omics data.In this study, we propose a novel framework for integrating unpaired single-cell multi-omics data based on the Kolmogorov-Arnold deep embedding model (scKADE). This framework introduces Kolmogorov–Arnold Networks (KAN) to map different omics data to a unified common feature space for adversarial alignment. Furthermore, to assess the contribution of different cells in data integration, we employ an unsupervised weight estimation algorithm to enhance the alignment quality.