Process / pipelineBioinformatics / omics

Single-Cell RNA-seq Differential Expression Analysis

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.

MethodMind'de açSoonVideoSoon

Tam yöntemi oku

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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. DOI: 10.1038/nbt.4096
  2. 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. DOI: 10.1186/s13059-014-0550-8

Related methods

Referenced by

ScholarGateSingle-cell RNA-seq differential expression (Single-Cell RNA Sequencing Differential Expression Analysis). Retrieved 2026-06-04 from https://scholargate.app/tr/bioinformatics/single-cell-rna-seq-differential-expression