Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Võrgupõhine üksikrakkude RNA-seq analüüs× | RNA-seq diferentsiaalne ekspressioon× | |
|---|---|---|
| Valdkond | Bioinformaatika | Bioinformaatika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017) | 2008–2010 (RNA-seq DE methodology established) |
| Looja≠ | 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) |
| Tüüp≠ | Computational bioinformatics pipeline | Quantitative genomics pipeline |
| Algallikas≠ | 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. link ↗ | 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. DOI ↗ |
| Rööpnimetused | 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 |
| Seotud | 6 | 6 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
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