방법 비교

	베이지안 RNA-seq 차등 발현	RNA-seq 차등 발현
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2010–2013	2008–2010 (RNA-seq DE methodology established)
창시자	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
유형	Bayesian statistical inference pipeline	Quantitative genomics pipeline
원전	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.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
별칭	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
관련	6	6
요약	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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.