scRNA-seq analysis using Seurat

The Seurat package is used for counts data pre-processing, feature selection, clustering and visualization.

Pre-requisites and installation

R (>3.4.0)

install.packages("Seurat")
library(Seurat)

Workflow

1. Load counts data and annotation data (from scRNA-seq pre-processing pipeline)
2. Create seurat object; filter cells
3. Generate QC data and plots
4. Filter based on number of features, counts and percent MT
5. Normalize and scale data
6. Identify variable features
7. PCA analysis and visualization of components
8. UMAP/TSNE Clustering and visualization
9. (Optional) Visualize features (markers, cell-type annotations)

Options/Arguments

Add parameters different from default

Updates

Add dataset-specific updates

Cluster parameters and runtime

Cluster: N/A Desktop: 1-2 hours

References and documentation

Reference: Butler et al. Nature Biotech, 2018

Vignette: Satija lab home page (https://satijalab.org/seurat/vignettes.html)