Porovnat metody

	Diferenciální analýza jednobuněčné RNA-seq	Analýza jednobuněčné RNA-seq
Obor	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2015–2021	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Tvůrce	Multiple contributors; pseudobulk framework formalized by Squair et al. (2021); Seurat/FindMarkers by Satija lab (~2015)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
Typ	Computational bioinformatics pipeline	High-throughput single-cell transcriptomic profiling pipeline
Původní zdroj	Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20, 296.	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.
Další názvy	scRNA-seq differential analysis, single-cell differential expression analysis, scDE analysis, single-cell comparative transcriptomics	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Příbuzné	3	5
Shrnutí	Differential single-cell RNA-seq (scRNA-seq) analysis is a computational pipeline that compares transcriptomic profiles across biological conditions — such as treated versus untreated, disease versus healthy, or time points — at single-cell resolution. It identifies which genes, cell types, and cell states change between conditions, providing mechanistic insight that bulk RNA-seq comparisons cannot offer. The approach combines clustering, cell-type annotation, and statistical testing, typically using pseudobulk aggregation to account for within-sample correlation.	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.