Порівняння методів

	Байєсівський аналіз протеоміки	Аналіз диференційної експресії генів методом RNA-seq
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2000s (major developments 2003–2010)	2008–2010 (RNA-seq DE methodology established)
Автор методу	Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleagues	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Тип	Probabilistic inference pipeline	Quantitative genomics pipeline
Основоположне джерело	Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925.	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.
Інші назви	Bayesian protein quantification, Bayesian peptide inference, probabilistic proteomics, Bayesian mass spectrometry analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Пов'язані	6	6
Підсумок	Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrated posterior probabilities of identification and quantification rather than simple point estimates, enabling more principled control of false discovery rates and more honest reporting of uncertainty than purely frequentist alternatives.	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.