Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Aegridade rajaprognooside analüüs× | RNA-seq diferentsiaalne ekspressioon× | |
|---|---|---|
| Valdkond | Bioinformaatika | Bioinformaatika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2005–2014 | 2008–2010 (RNA-seq DE methodology established) |
| Looja≠ | Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Tüüp≠ | Functional enrichment analysis with temporal modeling | Quantitative genomics pipeline |
| Algallikas≠ | Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168. 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 | temporal pathway analysis, longitudinal pathway enrichment, dynamic pathway analysis, TPEA | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Seotud≠ | 5 | 6 |
| Kokkuvõte≠ | Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses. | 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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