Department of Statistics, University of Wisconsin, Madison, WI 53706, USA.
Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792, USA.
Nucleic Acids Res. 2021 Dec 16;49(22):e127. doi: 10.1093/nar/gkab823.
Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present multilayer graph clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale benchmarking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.
单细胞转录组测序（scRNA-seq）使我们能够以更高的分辨率研究细胞异质性。鉴定在多种实验条件下的新型细胞类型或短暂的发育阶段是其主要应用之一。线性和非线性降维是从 scRNA-seq 数据中进行推断的基础工具。我们提出了多层图聚类（MLG）作为一种综合方法，用于整合多条件 scRNA-seq 数据的多种降维方法。MLG 生成了一个具有更高信噪比的多层共享最近邻细胞图，并且在大规模基准测试实验中，在聚类准确性方面优于当前的最佳实践。MLG 在来自多种条件的各种数据集上的应用突出了 MLG 如何提高精细亚群识别的信噪比。MLG 广泛适用于通过降维进行单细胞数据整合的场景。
