Comparar métodos

	Análisis bayesiano de enriquecimiento de conjuntos de genes	Análisis de Enriquecimiento de Conjuntos de Genes a Nivel de Célula Única
Campo	Bioinformática	Bioinformática
Familia	Process / pipeline	Process / pipeline
Año de origen	2004–2007	2017-2019
Autor original	Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework	Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)
Tipo	Probabilistic gene set enrichment method	Computational enrichment scoring pipeline
Fuente seminal	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... & 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.	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.
Alias	Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing	scGSEA, single-cell GSEA, cell-level gene set scoring, scRNA-seq pathway scoring
Relacionados	6	5
Resumen	Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GSEA, the Bayesian approach models uncertainty in expression estimates explicitly, incorporates prior biological knowledge, and produces posterior probabilities of enrichment rather than raw p-values, enabling more principled inference especially in small-sample settings.	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.