เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การวิเคราะห์การแสดงออกที่แตกต่างกันของ RNA-seq โดยใช้เครือข่าย× | Gene Set Enrichment Analysis (GSEA)× | |
|---|---|---|
| สาขาวิชา | ชีวสารสนเทศศาสตร์ | ชีวสารสนเทศศาสตร์ |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 2002–2005 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| ผู้ริเริ่ม≠ | Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| ประเภท≠ | Integrative computational pipeline | Functional genomics / enrichment analysis |
| แหล่งต้นตำรับ≠ | 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 ↗ | 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 ↗ |
| ชื่อเรียกอื่น | network-aware DE analysis, gene network differential expression, co-expression network DE, NB-DEA | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| ที่เกี่ยวข้อง | 5 | 5 |
| สรุป≠ | 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. | 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. |
| ScholarGateชุดข้อมูล ↗ |
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