A universal framework for single-cell multi-omics data integration with graph convolutional networks

Single-cell omics data are growing at an unprecedented rate, whereas effective integration of them remains challenging due to different sequencing methods, quality, and expression pattern of each omics data. In this study, we propose a universal framework for the integration of single-cell multi-omics data based on graph convolutional network (GCN-SC). Among the multiple single-cell data, GCN-SC usually selects one data with the largest number of cells as the reference and the rest as the query dataset. It utilizes mutual nearest neighbor algorithm to identify cell-pairs, which provide connections between cells both within and across the reference and query datasets. A GCN algorithm further takes the mixed graph constructed from these cell-pairs to adjust count matrices from the query datasets. Finally, dimension reduction is performed by using non-negative matrix factorization before visualization. By applying GCN-SC on six datasets, we show that GCN-SC can effectively integrate sequencing data from multiple single-cell sequencing technologies, species or different omics, which outperforms the state-of-the-art methods, including Seurat, LIGER, GLUER and Pamona.

Automated summary

In this study, a universal framework called GCN-SC is proposed for integrating single-cell multi-omics data. GCN-SC selects one dataset as the reference and the rest as the query datasets, and uses mutual nearest neighbor algorithm to identify cell-pairs that connect cells within and across datasets. Then, a GCN algorithm adjusts the count matrices from query datasets, followed by dimension reduction using non-negative matrix factorization.