The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Although different batch effect removal methods have been developed, none are suitable for heterogeneous single-cell samples coming from multiple biological conditions. We propose a method, scINSIGHT, to learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular identities and processes across single-cell samples. We compare scINSIGHT with state-of-the-art methods using simulated and real data, which demonstrate its improved performance. Our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.
CITATION STYLE
Qian, K., Fu, S., Li, H., & Li, W. V. (2022). scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data. Genome Biology, 23(1). https://doi.org/10.1186/s13059-022-02649-3
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