Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA.
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Bioinformatics. 2022 Jul 11;38(14):3642-3644. doi: 10.1093/bioinformatics/btac372.
Quantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data.
scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html.
Supplementary data are available at Bioinformatics online.
需要定量工具来利用单细胞高通量染色质构象（scHi-C）数据前所未有的分辨率，并将其与其他单细胞数据模式进行整合。我们提出了单细胞基因关联域（scGAD）评分，作为 scHi-C 数据的降维和探索性分析工具。scGAD 能够在考虑到固有基因水平基因组偏差的情况下，对基因单位进行总结。scGAD 的低维投影可以基于细胞的 3D 结构捕获细胞的聚类。可以通过 scGAD 识别细胞类型内和细胞类型之间的显著染色质相互作用。我们还表明，scGAD 通过将其投射到参考低维嵌入中来促进 scHi-C 数据与其他单细胞数据模式的集成。这种多模态数据集成通过自动细化细胞类型注释，为 scHi-C 数据提供了一种方法。
scGAD 是 BandNorm R 包的一部分，可在 https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html 上获得。
补充数据可在生物信息学在线获得。
