Comparing gene-gene co-expression network approaches for the analysis of cell differentiation and specification on scRNAseq data
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 process...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001990 |
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| Summary: | 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. |
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| ISSN: | 2001-0370 |