Comparer des méthodes

	Alignement de séquences unicellulaires	Analyse de l'expression différentielle par RNA-seq
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2013–2016	2008–2010 (RNA-seq DE methodology established)
Auteur d'origine	Adapted from bulk RNA-seq aligners; single-cell extensions by Dobin et al. (STAR) and 10x Genomics Cell Ranger team	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Type	Computational pipeline step	Quantitative genomics pipeline
Source fondatrice	Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21.	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.
Alias	scRNA-seq alignment, single-cell read mapping, scSeq alignment, cell barcode-aware alignment	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Apparentées	1	6
Résumé	Single-cell sequence alignment is the computational step that maps millions of short sequencing reads produced by single-cell RNA-seq experiments back to a reference genome or transcriptome. Unlike bulk RNA-seq alignment, each read carries a cell barcode and a Unique Molecular Identifier (UMI) that together identify the originating cell and the individual RNA molecule. Accurate alignment and barcode demultiplexing are prerequisites for constructing the cell-by-gene count matrix that drives all downstream single-cell analyses.	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.