手法を比較

	ベイズ的メタボロミクス解析	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	2005–2010	2008–2010 (RNA-seq DE methodology established)
提唱者	Simon Rogers, Mark Girolami and colleagues (Bayesian NMR metabolomics framework, ~2009); broader Bayesian metabolomics developed through 2000s–2010s	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
種類	Probabilistic statistical pipeline	Quantitative genomics pipeline
原典	Rogers, S., Scheltema, R. A., & Girolami, M. A. (2009). Bayesian analysis of metabolomic NMR data. Bioinformatics, 25(14), 1809-1815.	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.
別名	Bayesian metabolomics, probabilistic metabolomics, Bayesian metabolite profiling, Bayesian metabolic flux analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
関連	6	6
概要	Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributions and propagating uncertainty throughout the analysis, it yields more calibrated probability statements about metabolic differences than classical frequentist testing alone.	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.