Porovnat metody

	Bayesovská analýza eQTL	Analýza obohacení drah
Obor	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2000s–2010s	2003–2005
Tvůrce	Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Typ	Probabilistic genomic association method	Statistical functional annotation method
Původní zdroj	Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.	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 eQTL mapping, probabilistic eQTL analysis, Bayesian QTL mapping for gene expression, eQTL fine-mapping	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Příbuzné	6	6
Shrnutí	Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled fine-mapping of causal variants and coherent uncertainty quantification across thousands of gene-SNP pairs simultaneously.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.