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| Bayesian Gene Set Enrichment Analysis× | Single-cell Gen-Set-Anreicherung – scGSEA× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2004–2007 | 2017-2019 |
| Urheber≠ | Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework | Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION) |
| Typ≠ | Probabilistic gene set enrichment method | Computational enrichment scoring pipeline |
| Wegweisende Quelle≠ | 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 ↗ | Aibar, S., Gonzalez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Kalender Atak, Z., Wouters, J., & Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083-1086. link ↗ |
| Aliasnamen | Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing | scGSEA, single-cell GSEA, cell-level gene set scoring, scRNA-seq pathway scoring |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogeneous cell populations, cell states, and developmental trajectories captured in single-cell RNA-seq data. |
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