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	Single-cell RNA-seq differential expression	Analiza wzbogacenia zestawów genów (GSEA)
Dziedzina	Bioinformatyka	Bioinformatyka
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2013–2015 (first scRNA-seq DE tools; refined 2015–present)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Twórca	Pioneered through Seurat (Satija lab) and scde (Kharchenko lab) frameworks, building on bulk RNA-seq DE foundations	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Typ	Computational bioinformatics pipeline	Functional genomics / enrichment analysis
Źródło pierwotne	Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420.	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.
Inne nazwy	scRNA-seq DE, single-cell differential expression, scDE, cell-level differential expression analysis	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Pokrewne	5	5
Podsumowanie	Single-cell RNA-seq differential expression (scRNA-seq DE) analysis identifies genes whose expression levels differ significantly between defined groups of individual cells — such as cell types, disease states, or treatment conditions. Unlike bulk RNA-seq, which averages signals across millions of cells, scRNA-seq DE operates on the transcriptome of each individual cell, enabling fine-grained characterization of cell-population-specific gene regulation and heterogeneity within seemingly homogeneous tissue.	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.