Zhang Han, Lu Xinghua, Lu Binfeng, Chen Lujia
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA.
UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA.
Cancers (Basel). 2023 Aug 26;15(17):4277. doi: 10.3390/cancers15174277.
Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types.
In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells.
We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence ( < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration ( = 0.009) and the cell cycle ( < 0.001) than other methods in additional validation on the bulk RNAseq dataset.
Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells.
单细胞转录组分析通过对单个细胞和细胞类型子集进行更高分辨率的计算分析，从根本上改变了生物学研究。然而，很少有方法能够满足识别和量化决定细胞类型特化和分化的潜在细胞程序的需求。
在本研究中，我们提出了scGEM，这是一种嵌套树状结构的非参数贝叶斯模型，用于揭示反映单细胞转录组过程的基因共表达模块（GEMs）。
我们表明，scGEM可以使用外周血单核单细胞和早期脑发育单细胞发现不同细胞类型之间共享和特化的转录组信号。在我们的模拟数据上，scGEM在困惑度和主题连贯性方面（<0.001）优于其他方法。更大的数据集、更深的树和预训练模型与更好的scGEM性能呈正相关。在批量RNAseq数据集的额外验证中，从三阴性乳腺癌单细胞获得的GEMs与淋巴细胞浸润（=0.009）和细胞周期（<0.001）的相关性比其他方法更好。
总之，我们证明scGEM可用于对单细胞隐藏的细胞功能进行建模，从而揭示不同类型细胞中转录组程序的特化和泛化。
