পদ্ধতির তুলনা করুন

	এককোষী RNA-seq বিশ্লেষণের নেটওয়ার্ক-ভিত্তিক পদ্ধতি	জিন সেট এনরিচমেন্ট অ্যানালাইসিস (GSEA)
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
প্রবর্তক	Aibar et al. (SCENIC, gene regulatory networks); Jin et al. (CellChat, cell-cell communication networks)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
ধরন	Computational bioinformatics pipeline	Functional genomics / enrichment analysis
মৌলিক উৎস	Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., ... & Aerts, S. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086.	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.
অপর নাম	scRNA-seq network analysis, single-cell gene regulatory network inference, scGRN analysis, single-cell co-expression network analysis	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
সম্পর্কিত	6	5
সারসংক্ষেপ	Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, this approach captures the coordinated activity of gene circuits and intercellular signalling pathways within and between cell populations, enabling a systems-level view of transcriptional regulation at single-cell resolution.	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.