Single-cell Gene Regulation Network Interference by Large-scale Data Integration

Dong, X.; Tang, K.; Xu, Y.; Wei, H.; Han, T.; Wang, C.

doi:10.1101/2022.02.19.481131

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bioRxiv · 10.1101/2022.02.19.481131

Single-cell Gene Regulation Network Interference by Large-scale Data Integration

Dong, X.·Tang, K.·Xu, Y.·Wei, H.·Han, T.·Wang, C.

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Abstract

Single-cell ATAC-seq (scATAC-seq) has proven to be a state-of-art approach to investigating gene regulation at the single-cell level. However, existing methods cannot precisely uncover cell-type-specific binding of transcription regulators (TRs) and construct gene regulation networks (GRNs) in single-cell. ChIP-seq has been widely used to profile TR binding sites in the past decades. Here, we developed SCRIP, an integrative method to infer single-cell TR activity and targets based on the integration of scATAC-seq and a large-scale TR ChIP-seq reference. Our method showed improved performance in evaluating TR binding activity compared to the existing motif-based methods and reached a higher consistency with matched TR expressions. Besides, our method enables identifying TR target genes as well as building GRNs at the single-cell resolution based on a regulatory potential model. We demonstrate SCRIPs utility in accurate cell-type clustering, lineage tracing, and inferring cell-type-specific GRNs in multiple biological systems. SCRIP is freely available at https://github.com/wanglabtongji/SCRIP.

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Dong, X., Tang, K., Xu, Y., Wei, H., Han, T., Wang, C.. 2022-02-22. Single-cell Gene Regulation Network Interference by Large-scale Data Integration. https://doi.org/10.1101/2022.02.19.481131

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