Сравнение методов

	Сетевой анализ РНК-секвенирования единичных клеток	Анализ дифференциальной экспрессии РНК-сек (DE)
Область	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Год появления	2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017)	2008–2010 (RNA-seq DE methodology established)
Автор метода	Aibar et al. (SCENIC, gene regulatory networks); Jin et al. (CellChat, cell-cell communication networks)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Тип	Computational bioinformatics pipeline	Quantitative genomics pipeline
Основополагающий источник	Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., ... & Aerts, S. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
Другие названия	scRNA-seq network analysis, single-cell gene regulatory network inference, scGRN analysis, single-cell co-expression network analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Связанные	6	6
Сводка	Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, this approach captures the coordinated activity of gene circuits and intercellular signalling pathways within and between cell populations, enabling a systems-level view of transcriptional regulation at single-cell resolution.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.