השוואת שיטות
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| ניתוח ביטוי דיפרנציאלי בריצוף RNA רב-אומיי× | ניתוח העשרת קבוצת גנים (GSEA)× | |
|---|---|---|
| תחום | ביואינפורמטיקה | ביואינפורמטיקה |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 2010–2018 (core DE methods ~2010; multi-omics integration frameworks ~2014–2018) | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| הוגה השיטה≠ | Synthesised from DESeq2/edgeR (Anders & Huber 2010; Robinson et al. 2010) and multi-omics integration frameworks (Argelaguet et al. 2018) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| סוג≠ | Integrative computational pipeline | Functional genomics / enrichment analysis |
| מקור מכונן≠ | 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 ↗ | 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 ↗ |
| כינויים | multi-omics DE analysis, integrative RNA-seq DE, multi-layer differential expression, omics-integrated transcriptomics | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| קשורות≠ | 1 | 5 |
| תקציר≠ | Multi-omics RNA-seq differential expression analysis combines transcript-level count data from RNA sequencing with one or more additional omics layers — such as proteomics, metabolomics, epigenomics, or genomic variant data — to identify genes, proteins, or metabolites that differ systematically between biological conditions. By integrating multiple molecular levels, the pipeline captures regulatory mechanisms that transcriptomics alone cannot resolve, enabling a more complete picture of the biological processes driving observed phenotypes. | Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold. |
| ScholarGateמערך נתונים ↗ |
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