Linganisha mbinu

	Uchanganuzi wa Uimarishaji wa Kundi la Jeni la Bayesian	Uchanganuzi wa Utofauti wa Usemi wa Bayesian RNA-seq
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2004–2007	2010–2013
Mwanzilishi	Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)
Aina	Probabilistic gene set enrichment method	Bayesian statistical inference pipeline
Chanzo asilia	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.
Majina mbadala	Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq
Zinazohusiana	6	6
Muhtasari	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.