Porovnat metody

	Bayesovská analýza diferenciální exprese RNA-seq	Analýza obohacení genových sad (GSEA)
Obor	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2010–2013	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Tvůrce	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Typ	Bayesian statistical inference pipeline	Functional genomics / enrichment analysis
Původní zdroj	Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. (2013). EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics, 29(8), 1035–1043.	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.
Další názvy	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Příbuzné	6	5
Shrnutí	Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is differentially expressed, borrowing statistical strength across genes and naturally accommodating low sample sizes common in genomics experiments.	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.