Official release of Seurat 3.0
On April 16, 2019 - we officially updated the Seurat CRAN repository to release 3.0!
We have been working on this update for the past year, and are excited to introduce new features and functionality, in particular:
- Improved and expanded methods for single-cell integration. As described in Stuart*, Butler*, et al., Cell 2019 (bioRxiv preprint link here), Seurat v3 implements new methods to identify ‘anchors’ across diverse single-cell data types, in order to construct harmonized references, or to transfer information across experiments.
- Improved methods for normalization. Seurat v3 includes support for sctransform, a new modeling approach for the normalization of single-cell data, described in a second preprint. Compared to standard log-normalization, sctransform effectively removes technically-driven variation while preserving biological heterogeneity.
- Vignette: SCTransform vignette
- An efficiently restructured Seurat object, with an emphasis on multi-modal data. We have carefully re-designed the structure of the Seurat object, with clearer documentation, and a flexible framework to easily switch between RNA, protein, cell hashing, batch-corrected / integrated, or imputed data.
While we are excited for users to upgrade, we are committed to making this transition as smooth as possible, and to ensure that users can complete existing projects in Seurat v2 prior to upgrading:
- Users on all platforms can easily re-install Seurat v2, with detailed instructions here
- All website vignettes have been updated to v3, but v2 versions remain as well (look for the red button on the bottom-right of the screen).
- Seurat v3 includes an ‘UpgradeSeuratObject’ function, so old objects can be analyzed with the upgraded version.
- We include a command ‘cheat sheet’, a brief introduction to new commands, data accessors, visualization, and multiple assays in Seurat v3.0
- The command ‘cheat sheet’ also contains a translation guide between Seurat v2 and v3
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 in your research, please considering citing:
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, Tim Stuart, 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.
August 20, 2019 Version 3.1 released
- Support for SCTransform integration workflows
- Integration speed ups: reference-based integration + reciprocal PCA
April 16, 2019 Version 3.0 released
November 2, 2018 Version 3.0 alpha released
- Preprint published describing new methods for identifying ‘anchors’ across single-cell datasets
March 23, 2018 Version 2.3 released
- Improvements for speed and memory efficiency
- New vignette for analyzing ~250,000 cells from the Microwell-seq Mouse Cell Atlas dataset
January 10, 2018: Version 2.2 released
- Support for multiple-dataset alignment
- New methods for evaluating alignment performance
October 16, 2017: Version 2.1 released
- Support for multimodal single-cell data
- Support for MAST and DESeq2 packages for differential expression testing
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