방법 비교

	단일 세포 유전자 집합 농축 분석	RNA-seq 차등 발현
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2017-2019	2008–2010 (RNA-seq DE methodology established)
창시자	Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
유형	Computational enrichment scoring pipeline	Quantitative genomics pipeline
원전	Aibar, S., Gonzalez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Kalender Atak, Z., Wouters, J., & Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083-1086.	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.
별칭	scGSEA, single-cell GSEA, cell-level gene set scoring, scRNA-seq pathway scoring	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
관련	5	6
요약	Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogeneous cell populations, cell states, and developmental trajectories captured in single-cell RNA-seq data.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.