Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Aegridade geenikomplekti rikastamise analüüs× | RNA-seq diferentsiaalne ekspressioon× | |
|---|---|---|
| Valdkond | Bioinformaatika | Bioinformaatika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2005 (GSEA foundation); time-series adaptations 2007–2014 | 2008–2010 (RNA-seq DE methodology established) |
| Looja≠ | Extension of GSEA (Subramanian et al., 2005); time-series adaptations developed through maSigPro (Conesa lab) and related tools | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Tüüp≠ | Gene set enrichment method for longitudinal omics data | Quantitative genomics pipeline |
| Algallikas≠ | Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. DOI ↗ | 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 | longitudinal GSEA, dynamic GSEA, time-course GSEA, TS-GSEA | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Seotud | 6 | 6 |
| Kokkuvõte≠ | Time-series gene set enrichment analysis (TS-GSEA) extends the classical GSEA framework to detect biologically coordinated gene sets — pathways, gene ontology terms, or curated signatures — whose collective expression changes meaningfully over time. Rather than comparing two snapshots, it models the full temporal trajectory of gene expression to identify which functional programs are activated, suppressed, or dynamically remodelled during a biological process such as development, treatment response, or disease progression. | 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. |
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