Aligning single-cell transcriptomes is important for the joint analysis of multiple single-cell RNA sequencing datasets, which in turn is vital to establishing a holistic cellular landscape of certain biological processes. Although numbers of approaches have been proposed for this problem, most of which only consider mutual neighbors when aligning the cells without taking into account known cell type annotations.
In this work, we present MAT2 that aligns cells in the manifold space with a deep neural network employing contrastive learning strategy. Compared with other manifold-based approaches, MAT2 has two-fold advantages. Firstly, with cell triplets defined based on known cell type annotations, the consensus manifold yielded by the alignment procedure is more robust especially for datasets with limited common cell types. Secondly, the batch effect-free gene expression reconstructed by MAT2 can better help annotate cell types. Benchmarking results on real scRNA-seq datasets demonstrate that MAT2 outperforms existing popular methods. Moreover, with MAT2, the hematopoietic stem cells are found to differentiate at different paces between human and mouse.