Σύγκριση μεθόδων

	Ανάλυση Βαϋεσιανής Εκφραστικότητας RNA-seq	Ανάλυση RNA-seq Μοναδιαίων Κυττάρων
Πεδίο	Βιοπληροφορική	Βιοπληροφορική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2010–2013	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Δημιουργός	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Τύπος	Bayesian statistical inference pipeline	High-throughput single-cell transcriptomic profiling pipeline
Θεμελιώδης πηγή	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.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
Εναλλακτικές ονομασίες	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Συναφείς	6	5
Σύνοψη	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.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.