Порівняння методів

	Диференційний аналіз одноклітинної РНК-секвенування	Аналіз диференційної експресії генів методом RNA-seq
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2015–2021	2008–2010 (RNA-seq DE methodology established)
Автор методу	Multiple contributors; pseudobulk framework formalized by Squair et al. (2021); Seurat/FindMarkers by Satija lab (~2015)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Тип	Computational bioinformatics pipeline	Quantitative genomics pipeline
Основоположне джерело	Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20, 296.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
Інші назви	scRNA-seq differential analysis, single-cell differential expression analysis, scDE analysis, single-cell comparative transcriptomics	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Пов'язані	3	6
Підсумок	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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.