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Single Cell RNA Analysis Overview

This section covers the analysis pipeline for single cell sequencing data, including scRNA-seq and related modalities.

What is Single Cell Analysis?

Single cell sequencing allows gene expression (and other molecular features) to be measured in individual cells rather than in bulk. This reveals cellular heterogeneity, rare cell populations, and developmental trajectories that are masked in bulk assays.

Common Modalities

  • scRNA-seq - Single cell RNA sequencing (10x Genomics Chromium, Smart-seq2)
  • snRNA-seq - Single nucleus RNA sequencing, suitable for frozen/fixed tissue
  • scATAC-seq - Chromatin accessibility at single cell resolution
  • Multiome - Simultaneous RNA + ATAC from the same cell

Workflow Steps

  1. Library QC - FastQC, MultiQC on raw reads
  2. Alignment & Quantification - Cell Ranger (10x), STARsolo, Salmon/Alevin
  3. Quality Filtering - Remove low-quality cells, doublets (Scrublet, DoubletFinder)
  4. Normalisation - Scran, SCTransform
  5. Dimensionality Reduction - PCA, UMAP, tSNE
  6. Clustering - Graph-based clustering (Louvain, Leiden)
  7. Cell Type Annotation - Marker genes, automated tools (SingleR, Azimuth)
  8. Differential Expression - Between clusters or conditions

Applications

  • Characterising cell type composition in tissues
  • Identifying rare or novel cell populations
  • Mapping developmental or differentiation trajectories
  • Comparing cell states across conditions or time points

Typical Outputs

  • Feature-barcode matrices (raw counts)
  • Seurat or AnnData objects
  • UMAP/tSNE plots
  • Marker gene lists
  • Differential expression results