Bandingkan metode

	Analisis RNA-seq Sel Tunggal Diferensial	Analisis RNA-seq Sel Tunggal
Bidang	Bioinformatika	Bioinformatika
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2015–2021	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Pencetus	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)
Tipe	Computational bioinformatics pipeline	High-throughput single-cell transcriptomic profiling pipeline
Sumber perintis	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.
Alias	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
Terkait	3	5
Ringkasan	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.