手法を比較

	ベイズ経路富化解析	ベイズRNA-seq差次的発現
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2001–2007	2010–2013
提唱者	Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)
種類	Probabilistic gene-set testing	Bayesian statistical inference pipeline
原典	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.	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.
別名	Bayesian gene-set testing, Bayesian GSEA, Bayesian functional enrichment, BGSEA	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq
関連	6	6
概要	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.	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.