方法证据记录
Bayesian Gene Set Enrichment Analysis
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Gene Set Enrichment Analysis
分类方法记录 · process-pipeline / bioinformatics
- 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. · DOI 10.1073/pnas.0506580102
- Newton, M. A., Quintana, F. A., Den Boon, J. A., Bhattacharya, S., & Ahlquist, P. (2007). Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. The Annals of Applied Statistics, 1(1), 85-106. · URL
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