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Abstract：
Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.
摘要：
单细胞转录组的基因共表达分析，旨在定义基因之间的功能关系，由于过多的dropout值而具有挑战性。这里,我们开发了一种单细胞图形高斯模型（SingleCellGGM）算法来进行单细胞基因共表达网络分析。当应用于小鼠单细胞数据集时，SingleCellGGM构建了网络，从中鉴定了具有高度显着功能富集的基因共表达模块。我们将这些模块视为基因表达程序(GEP)。这些GEP可以直接对单个细胞进行细胞类型注释，而无需细胞聚类，它们富含相应细胞功能所需的基因，有时水平超过10倍。GEP在数据集之间是保守的，并且能够在不同的研究之间进行通用的细胞类型标签转移。我们还提出了一种通过GEP平均进行单细胞分析的降维方法，提高结果的可解释性。因此，SingleCellGGM提供基于GEP的独特视角来分析单细胞转录组，并揭示不同单细胞数据集共享的生物学见解。
