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Single Cell Integration in Seurat v3.0

In our recent preprint, we introduce new methods into Seurat for single cell data integration. These include updated methods to integrate (or ‘assemble’) datasets into a common reference, as well as to transfer information from reference to query datasets.

Alongside this new functionality, we have made significant improvements to the Seurat object, accessor functions, and plotting library. In particular, we have upgraded the Seurat object to flexibly store multiple data types (‘assays’), from the same cells, and to allow the user to easily switch between them. We are currently preparing a full release of Seurat 3.0 on CRAN, with updated documentation, tutorials, and vignettes.

Here, we provide a pre-release of Seurat v3, with a brief vignette to enable users to explore our new methods, and to test them on their own datasets. While additional documentation and examples are forthcoming, please check out the following resources:

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.

News

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