Compara mètodes

	Anàlisi Bayesiana d'Enriquiment de Vies	Anàlisi d'Enriquiment de Vies Multiòmiques
Camp	Bioinformàtica	Bioinformàtica
Família	Process / pipeline	Process / pipeline
Any d'origen	2001–2007	2014–2016 (multi-omics extension of enrichment methods established ~2005)
Autor original	Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)	Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)
Tipus	Probabilistic gene-set testing	Integrative pathway analysis pipeline
Font seminal	Baldi, P., & Long, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6), 509–519.	Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162.
Àlies	Bayesian gene-set testing, Bayesian GSEA, Bayesian functional enrichment, BGSEA	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA
Relacionats	6	1
Resum	Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and updates it with the observed expression data, yielding posterior probabilities of enrichment rather than p-values. This probabilistic framing naturally handles small samples, multiple pathways, and uncertainty propagation in a coherent statistical framework.	Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss.