Compara mètodes

	Anàlisi Bayesià de l'Expressió Diferencial en RNA-seq	Anàlisi de RNA-seq de cèl·lules individuals
Camp	Bioinformàtica	Bioinformàtica
Família	Process / pipeline	Process / pipeline
Any d'origen	2010–2013	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Autor original	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Tipus	Bayesian statistical inference pipeline	High-throughput single-cell transcriptomic profiling pipeline
Font seminal	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.
Àlies	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Relacionats	6	5
Resum	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.