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Integrated Analysis of scRNA-seq datasets

For installation instructions, documentation, and tutorials for our scRNA-seq data integration methods (Butler and Satija, biorXiv, 2017), please click here.

About Seurat

Seurat is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. It easily enables widely-used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.

Seurat also features two 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)

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.


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