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. We also provide command lists used to apply this workflow to larger datasets, including a 33,000 PBMC dataset also from 10X genomics (download raw data, and final Seurat object), a dataset of 8,500 single cells from human pancreas made available by the Yanai lab. (download raw data and final Seurat object).
Seurat v2.0 implements new computational methods for integrated analysis of single cell datasets generated across different conditions, technologies, or even species. As an example, we provide a guided walkthrough for integrating PBMC datasets produced by different scRNA-seq technologies. We also provide command lists for aligning two additional published dataset pairs, as discussed in the manuscript (download R scripts, and expression matrices required to run them).
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.