Сравнение методов

	Байесовский анализ обогащения генных наборов	Байесовский дифференциальный анализ экспрессии РНК-секвенирования
Область	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Год появления	2004–2007	2010–2013
Автор метода	Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)
Тип	Probabilistic gene set enrichment method	Bayesian statistical inference pipeline
Основополагающий источник	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... & Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550.	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.
Другие названия	Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq
Связанные	6	6
Сводка	Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GSEA, the Bayesian approach models uncertainty in expression estimates explicitly, incorporates prior biological knowledge, and produces posterior probabilities of enrichment rather than raw p-values, enabling more principled inference especially in small-sample settings.	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.