পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| নেটওয়ার্ক-ভিত্তিক জিন সেট এনরিচমেন্ট অ্যানালাইসিস× | জিন সেট এনরিচমেন্ট অ্যানালাইসিস (GSEA)× | |
|---|---|---|
| ক্ষেত্র | জৈব তথ্যবিজ্ঞান | জৈব তথ্যবিজ্ঞান |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 2010 (NetGSA); field consolidated 2010-2015 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| প্রবর্তক≠ | Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| ধরন≠ | Network-informed statistical enrichment test | Functional genomics / enrichment analysis |
| মৌলিক উৎস≠ | Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22. 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 ↗ |
| অপর নাম | network GSEA, network-propagation GSEA, NetGSA, graph-informed gene set testing | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| সম্পর্কিত | 5 | 5 |
| সারসংক্ষেপ≠ | Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression signals across network edges, allowing genes that are co-regulated or functionally connected to jointly support the significance of a gene set. The result is a biologically coherent enrichment score that accounts for pathway topology and gene-gene dependencies. | 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. |
| ScholarGateডেটাসেট ↗ |
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