Comparer des méthodes

	Analyse bayésienne de l'ARN messager de cellule unique par séquençage	Régression binomiale négative
Domaine	Bio-informatique	Économétrie
Famille	Process / pipeline	Regression model
Année d'origine	2018 (scVI landmark); Bayesian scRNA-seq approaches emerged 2015-2018	2011
Auteur d'origine	Romain Lopez, Nir Yosef and Michael I. Jordan (scVI framework); preceded by Bayesian single-cell methods from Kharchenko, Markowetz, and others	Hilbe (textbook treatment); generalized linear model framework
Type	Probabilistic generative modeling pipeline	Generalized linear model for count data
Source fondatrice	Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	Bayesian scRNA-seq, scRNA-seq Bayesian modeling, probabilistic single-cell transcriptomics, Bayesian single-cell genomics	NB regression, NB2 regression, negatif binom regresyonu
Apparentées	3	4
Résumé	Bayesian single-cell RNA-seq analysis applies probabilistic generative models to the sparse, overdispersed count matrices produced by single-cell RNA sequencing. By placing prior distributions over latent biological variables — cell state, batch effects, dropout — the framework propagates uncertainty through every downstream inference step. Tools such as scVI, SCVI-tools, and BayesPrism implement this paradigm, enabling principled cell clustering, differential expression testing, and batch integration that explicitly models technical noise rather than ignoring it.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.