scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics

Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. 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. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. 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. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN.

scJoint: transfer learning for data integration of single-cell RNA-seq and ATAC-seq

Single-cell multi-omics data continues to grow at an unprecedented pace, and while integrating different modalities holds the promise for better characterisation of cell identities, it remains a significant computational challenge. In particular, extreme sparsity is a hallmark in many modalities such as scATAC-seq data and often limits their power in cell type identification. Here we present scJoint, a transfer learning method to integrate heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint uses a neural network to simultaneously train labelled and unlabelled data and embed cells from both modalities in a common lower dimensional space, enabling label transfer and joint visualisation in an integrative framework. We demonstrate scJoint consistently provides meaningful joint visualisations and achieves significantly higher label transfer accuracy than existing methods using a complex cell atlas data and a biologically varying multi-modal data. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.