方法对比

	贝叶斯单细胞RNA测序分析	潜在狄利克雷分配 (LDA)
领域	生物信息学	机器学习
方法族	Process / pipeline	Latent structure
起源年份	2018 (scVI landmark); Bayesian scRNA-seq approaches emerged 2015-2018	2003
提出者	Romain Lopez, Nir Yosef and Michael I. Jordan (scVI framework); preceded by Bayesian single-cell methods from Kharchenko, Markowetz, and others	Blei, D. M.; Ng, A. Y.; Jordan, M. I.
类型	Probabilistic generative modeling pipeline	Generative probabilistic topic model (three-level hierarchical Bayesian)
开创性文献	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
别名	Bayesian scRNA-seq, scRNA-seq Bayesian modeling, probabilistic single-cell transcriptomics, Bayesian single-cell genomics	LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling
相关	3	3
摘要	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.	Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.