Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Expresión Diferencial de RNA-seq Basado en Redes× | Análisis de scRNA-seq× | |
|---|---|---|
| Campo | Bioinformática | Bioinformática |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2002–2005 | 2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016 |
| Autor original≠ | Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework) | Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015) |
| Tipo≠ | Integrative computational pipeline | High-throughput single-cell transcriptomic profiling pipeline |
| Fuente seminal≠ | Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article 17. link ↗ | Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502. DOI ↗ |
| Alias | network-aware DE analysis, gene network differential expression, co-expression network DE, NB-DEA | scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling |
| Relacionados | 5 | 5 |
| Resumen≠ | Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meaningful gene modules that change together between conditions. This approach substantially reduces false positives and surfaces pathway-level signals invisible to gene-by-gene testing. | Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations. |
| ScholarGateConjunto de datos ↗ |
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