เปรียบเทียบวิธี

	การวิเคราะห์การเสริมเส้นทางชีวภาพแบบเบย์ (Bayesian Pathway Enrichment Analysis)	Bayesian RNA-seq differential expression
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2001–2007	2010–2013
ผู้ริเริ่ม	Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)
ประเภท	Probabilistic gene-set testing	Bayesian statistical inference pipeline
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Bayesian gene-set testing, Bayesian GSEA, Bayesian functional enrichment, BGSEA	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq
ที่เกี่ยวข้อง	6	6
สรุป	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.