DataSheet_4_MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data.csv

The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc.

基因交互网络中的功能模块识别，是解析生物学过程的关键环节。网络解析对于揭示生物学机制、候选生物标志物，或是药物研发与重定位的潜在靶点而言至关重要。尽管已有大量生物模块识别算法问世，但尚无算法专门针对单细胞RNA测序（single-cell RNA sequencing, scRNA-seq）数据开展此类任务。在此我们推出MTGO-SC——本团队开发的生物网络模块检测算法MTGO针对scRNA-seq数据的适配版本。MTGO-SC通过综合利用网络拓扑结构与节点（基因）的特征注释信息，识别基因功能模块。此类注释信息由外部数据源提供，例如各类数据库与文献库（如基因本体（Gene Ontology）、Reactome数据库）。得益于单细胞数据的高维度特性，我们可为每个样本中的每一类细胞簇（通常为细胞类型或细胞状态）单独构建网络；而传统批量RNA测序（bulk RNA-seq）则是对整个样本进行平均后再构建单一基因网络。MTGO-SC提供两种解析层级：基因间交互网络与模块间交互网络。MTGO-SC支持用户自定义基因网络提取规则，且可与Seurat单细胞RNA测序分析流程无缝集成。MTGO-SC的开源代码可从https://github.com/ne1s0n/MTGOsc获取。