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About Seurat

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.

If you use Seurat v2.0 or above in your research, please considering citing Butler et al., Nature Biotechnology 2018. Seurat features three recently developed computational methods for single cell analysis:

  1. Unsupervised clustering and discovery of cell types and states (Macosko, Basu, Satija et al., Cell, 2015)
  2. Spatial reconstruction of single cell data (Satija*, Farrell* et al., Nature Biotechnology, 2015)
  3. Integrated analysis of single cell RNA-seq across conditions, technologies, and species (Butler et al., Nature Biotechnology, 2018)

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.


March 23, 2018 Version 2.3 released

January 10, 2018: Version 2.2 released

October 16, 2017: Version 2.1 released

July 26, 2017: Version 2.0 released

October 4, 2016: Version 1.4 released

August 22, 2016: Version 1.3 released

May 21, 2015: Drop-Seq manuscript published. Version 1.2 released

April 13, 2015: Spatial mapping manuscript published. Version 1.1 released