![]() ![]() As more and more single-cell datasets become available, there is an urgent need to leverage existing and newly generated data in a reliable and reproducible way, learning from the established single-cell data with well-defined labels as reference, and transferring labels to newly generated datasets to assign cell-level annotations 10, 11. The rapid advances in single-cell technologies have led to remarkable growth of single cell omics data. These technological developments allow profiling of multiple molecular layers at single-cell resolution and assaying cells from multiple samples under different conditions. Other single-cell-based assays, include Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), profile cellular heterogeneity at the epigenetic level 9, 10, 11, which further elucidates transcriptional regulators 8, 12. ![]() Leading this wave of omics is single-cell RNA sequencing (scRNA-seq), which allows measurement of the transcriptome in thousands of single cells from multiple biological samples under various conditions 4, 5, 6, 7, 8. ![]() Single-cell omics technologies are increasingly used in biomedical research to provide high resolution insights into the complex cellular ecosystem and underlying molecular interconnectedness 1, 2, 3. scGCN is implemented as an integrated workflow as a python software, which is available at. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. ![]()
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