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bioRxiv · 10.1101/2021.10.13.464306

scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

Qian, K.·Fu, S.·Li, H.·Li, W. V.
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Abstract

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for het-erogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

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Qian, K., Fu, S., Li, H., Li, W. V.. 2021-10-13. scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data. https://doi.org/10.1101/2021.10.13.464306

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