Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) is a high-throughput genomics technique that measures gene expression at the individual cell level, enabling the analysis of cellular heterogeneity, identification of rare cell types, and characterization of developmental trajectories. It involves isolating single cells, capturing their RNA, converting it to cDNA, and sequencing to quantify transcriptomes, providing insights into cellular diversity and function in tissues, organs, or organisms.

Also known as: scRNA-seq, Single Cell RNA-Seq, Single-Cell Transcriptomics, scRNAseq, Single Cell Sequencing

Why learn Single-Cell RNA Sequencing?

Developers should learn scRNA-seq when working in bioinformatics, computational biology, or biomedical research to analyze complex biological systems, such as cancer, immunology, or developmental biology, where understanding cell-to-cell variation is critical. It is used for applications like cell type discovery, differential expression analysis, and trajectory inference, requiring skills in data processing, statistical modeling, and visualization to handle large-scale datasets.

Learning Resources

• docs: Orchestrating Single-Cell Analysis with Bioconductor
• tutorial: Single Cell RNA-seq Analysis Course by Harvard Chan Bioinformatics Core
• course: Introduction to Single-Cell RNA-seq Analysis on Coursera
• video: Single-Cell RNA Sequencing Explained by StatQuest
• docs: Analysis of Single Cell RNA-seq Data by Oshlack et al.