Seurat is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data.
Seurat features three recently developed computational methods for single cell analysis:
- Unsupervised clustering and discovery of cell types and states (Macosko, Basu, Satija et al., Cell, 2015)
- Updated approach: Combining dimensional reduction with graph-based clustering
- Tutorial: Unsupervised identification of immune cell types and biomarkers from 2,700 PMBCs (10X Chromium)
- Spatial reconstruction of single cell data (Satija*, Farrell* et al., Nature Biotechnology, 2015)
- Integrates single cell RNA-seq with in situ reference data to infer cellular spatial localization from gene expression.
- Tutorial: Inferring spatial localization of single cells during Zebrafish embryogenesis
- Integrated analysis of single cell RNA-seq across conditions, technologies, and species (Butler and Satija, biorXiv, 2017)
- Unsupervised ‘alignment’ of shared cell types between single cell datasets, based on common sources of variation
- Tutorial: Aligning PBMC datasets generated with 10X Genomics and SeqWell
All methods emphasize clear, attractive, and interpretable visualizations, and were designed to be easily used by both dry-lab and wet-lab researchers.
Seurat is developed and maintained by the Satija lab, in particular by Andrew Butler, Paul Hoffman, Christoph Hafemeister, and Shiwei Zheng, and is released under the GNU Public License (GPL 3.0). We are also grateful for significant ideas and code from Jeff Farrell, Karthik Shekhar, and other generous contributors.
July 26, 2017: Version 2.0 released
- Preprint published for integrated analysis of scRNA-seq datasets
- New methods for dataset integration, visualization, and exploration
- Significant restructuring of codebase to emphasize clarity and clear documentation
October 4, 2016: Version 1.4 released
- Added methods for negative binomial regression and differential expression testing for UMI count data
- New ways to merge and downsample Seurat objects
August 22, 2016: Version 1.3 released
- Improved clustering approach - see FAQ for details
- All functions support sparse matrices
- Methods for removing unwanted sources of variation
- Consistent function names
- Updated visualizations
May 21, 2015: Drop-Seq manuscript published. Version 1.2 released
- Added support for spectral t-SNE (non-linear dimensional reduction), and density clustering
- New visualizations - including pcHeatmap, dot.plot, and feature.plot
- Expanded package documentation, reduced import package burden
- Seurat code is now hosted on GitHub, enables easy install through devtools package
- Small bug fixes
April 13, 2015: Spatial mapping manuscript published. Version 1.1 released