Pavel Alisa, Grønberg Manja Gersholm, Clemmensen Line H
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
Department of Mathematical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark.
Comput Struct Biotechnol J. 2025 Jun 6;27:2747-2756. doi: 10.1016/j.csbj.2025.05.040. eCollection 2025.
Gene-gene co-expression network analysis has been widely applied to bulk RNA sequencing and microarray data to investigate different phenotypes and compound exposures. Recently, it has also been applied to single cell RNA sequencing data. However, the impact of different network models, data processing pipelines, and analysis strategies on downstream interpretations has not yet been characterized. Here we study the impact of network models and analysis strategies on the resulting interpretations from analyses of cell differentiation and cell state over time using gene-gene co-expression networks. Our results suggest that the network modeling choice has less impact on downstream results than the network analysis strategy selected. The largest differences in biological interpretation were observed between the node-based and community-based network analysis methods (strategies). In addition, we observe a difference between single time point and combined time point modeling.
基因-基因共表达网络分析已广泛应用于批量RNA测序和微阵列数据，以研究不同的表型和复合暴露。最近，它也被应用于单细胞RNA测序数据。然而，不同的网络模型、数据处理流程和分析策略对下游解释的影响尚未得到描述。在这里，我们使用基因-基因共表达网络研究网络模型和分析策略对细胞分化和细胞状态随时间分析结果解释的影响。我们的结果表明，网络建模选择对下游结果的影响小于所选的网络分析策略。在基于节点和基于社区的网络分析方法（策略）之间观察到生物学解释的最大差异。此外，我们观察到单时间点建模和组合时间点建模之间的差异。
