Getting Started with Seurat
The input to Seurat is a gene expression matrix, where the rows are genes and the columns are single cells. To get started, first install the software and load the package library.
For new users of Seurat, we suggest starting with a guided walkthrough of a dataset of 2,700 Peripheral Blood Mononuclear Cells (PBMCs) made publically available by 10X Genomics (download raw data, R markdown file, and final Seurat object). This tutorial implements the major components of the Seurat clustering workflow including QC and data filtration, calculation of high-variance genes, dimensional reduction, graph-based clustering, and the identification of cluster markers.
Recently, we have developed new computational methods for integrated analysis of single cell datasets generated across different conditions, technologies, or species. As an example, we provide a guided walkthrough for integrating and comparing PBMC datasets generated under different stimulation conditions. We also provide a workflow tailored to the analysis of large datasets (250,000 cells from a recently published study of the Microwell-seq Mouse Cell Atlas), as well as an example analysis of multimodal single cell data.
Previous Seurat Versions
Tutorials for Seurat versions 1.3-1.4 can be found here.
Tutorials for Seurat version <= 1.2 can be found here.
All current and previous versions of Seurat can be found on github.