Bandingkan metode

	Analisis Ekspresi Diferensial Ekspresi Gen RNA-seq Sel Tunggal	Analisis RNA-seq Sel Tunggal
Bidang	Bioinformatika	Bioinformatika
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2013–2015 (first scRNA-seq DE tools; refined 2015–present)	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
Pencetus	Pioneered through Seurat (Satija lab) and scde (Kharchenko lab) frameworks, building on bulk RNA-seq DE foundations	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	Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420.	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 DE, single-cell differential expression, scDE, cell-level differential expression analysis	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
Terkait	5	5
Ringkasan	Single-cell RNA-seq differential expression (scRNA-seq DE) analysis identifies genes whose expression levels differ significantly between defined groups of individual cells — such as cell types, disease states, or treatment conditions. Unlike bulk RNA-seq, which averages signals across millions of cells, scRNA-seq DE operates on the transcriptome of each individual cell, enabling fine-grained characterization of cell-population-specific gene regulation and heterogeneity within seemingly homogeneous tissue.	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.