scJoint: Transfer Learning for Combined Annotated CITE-seq and ASAP-seq Data

Lin et al.

scJoint leverages information from annotated scRNA-seq data in a semisupervised framework and uses a neural network to simultaneously train labeled and unlabeled data, allowing label transfer and joint visualization in an integrative framework. Using atlas data as well as multimodal datasets generated with ASAP-seq and CITE-seq, we demonstrate that scJoint is computationally efficient and consistently achieves substantially higher cell-type label accuracy than existing methods while providing meaningful joint visualizations. Thus, scJoint overcomes the heterogeneity of different data modalities to enable a more comprehensive understanding of cellular phenotypes.

Citation:
Lin, Yingxin, et al. scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq. BioRxiv (2021): 2020-12.
Released: Jun-28-2022
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Single-Cell Bioinformatics
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