
Seurat v3.2.0
Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.
Instructions, documentation, and tutorials can be found at:
- https://satijalab.org/seurat
Seurat is also hosted on GitHub, you can view and clone the repository at
- https://github.com/satijalab/seurat
Seurat has been successfully installed on Mac OS X, Linux, and Windows, using the devtools package to install directly from GitHub
Improvements and new features will be added on a regular basis, please contact seuratpackage@gmail.com with any questions or if you would like to contribute
Version History
July 15, 2020
- Version 3.2
- Changes:
- Support for analysis and visualization of spatially resolved datasets
August 20, 2019
- Version 3.1
- Changes:
- Support for SCTransform integration workflows
- Integration speed ups: reference-based integration + reciprocal PCA
April 12, 2019
- Version 3.0
- Changes:
- Preprint published describing new methods for identifying anchors across single-cell datasets
- Restructured Seurat object with native support for multimodal data
- Parallelization support via future
July 20, 2018
- Version 2.4
- Changes:
- Java dependency removed and functionality rewritten in Rcpp
March 22, 2018
- Version 2.3
- Changes:
- New utility functions
- Speed and efficiency improvments
January 10, 2018
- Version 2.2
- Changes:
- Support for multiple-dataset alignment with RunMultiCCA and AlignSubspace
- New methods for evaluating alignment performance
October 12, 2017
- Version 2.1
- Changes:
- Support for using MAST and DESeq2 packages for differential expression testing in FindMarkers
- Support for multi-modal single-cell data via @assay slot
July 26, 2017
- Version 2.0
- Changes:
- Preprint released for integrated analysis of scRNA-seq across conditions, technologies and species
- Significant restructuring of code to support clarity and dataset exploration
- Methods for scoring gene expression and cell-cycle phase
October 4, 2016
- Version 1.4 released
- Changes:
- Improved tools for cluster evaluation/visualizations
- Methods for combining and adding to datasets
August 22, 2016:
- Version 1.3 released
- Changes :
- 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
- Changes :
- Added support for spectral t-SNE 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
- Small bug fixes
April 13, 2015:
- Spatial mapping manuscript published. Version 1.1 released (initial release)