Single Cell RNA Sequencing

Single Cell RNA Sequencing (scRNA-seq) is a high-throughput genomic technique that measures gene expression at the resolution of individual cells, enabling the characterization of cellular heterogeneity, identification of rare cell types, and analysis of developmental trajectories. It involves isolating single cells, reverse transcribing their RNA into cDNA, amplifying it, and sequencing to quantify transcript levels, providing insights into cell states, functions, and interactions in complex tissues.

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

Why learn Single Cell RNA Sequencing?

Developers should learn scRNA-seq when working in bioinformatics, computational biology, or biomedical data science to analyze cellular diversity in health and disease, such as in cancer research, immunology, or developmental biology. It is essential for building pipelines to process raw sequencing data, perform quality control, clustering, differential expression analysis, and visualization, often using tools like Seurat or Scanpy, to derive biological insights from large-scale datasets.

Learning Resources

• docs: Orchestrating Single-Cell Analysis with Bioconductor
• tutorial: Scanpy Tutorial: Single-Cell RNA-seq Analysis
• tutorial: Seurat Guided Clustering Tutorial
• course: Coursera: Single Cell RNA Sequencing Data Analysis
• video: YouTube: Introduction to Single-Cell RNA-Seq