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Guided Analyses

For new users of Seurat, we suggest starting with a guided walkthrough of a dataset of 2,700 Peripheral Blood Mononuclear Cells (PBMCs) made publicly available by 10X Genomics (download raw data, R markdown file, and final Seurat object). This tutorial implements the major components of the Seurat clustering workflow including QC and data filtration, calculation of high-variance genes, dimensional reduction, graph-based clustering, and the identification of cluster markers.

Recently, we have developed new computational methods for integrated analysis of single-cell datasets generated across different conditions, technologies, or species. As an example, we provide a guided walkthrough for integrating and comparing PBMC datasets generated under different stimulation conditions. We also provide a workflow tailored to the analysis of large datasets (250,000 cells from a recently published study of the Microwell-seq Mouse Cell Atlas), as well as an example analysis of multimodal single-cell data.

Guided tutorial --- 2,700 PBMCs

A basic overview of Seurat that includes an introduction to:

  • QC and pre-processing
  • Dimension reduction
  • Clustering
  • Differential expression

Multiple Dataset Integration and Label Transfer

Learn about the new anchoring framework in Seurat v3:

  • Integrate multiple scRNA-seq datasets across technologies
  • Transfer labels across datasets

Analysis of spatial datasets

Analysis of spatially-resolved transcriptomic data.

  • Preprocessing and normalization
  • Interactive visualization
  • Integration with scRNA-seq data

Mouse Cell Atlas, 250K cells

An example of working with large datasets in Seurat:

  • cluster and visualize ~250K cells
  • suggestions for speed and memory efficiency

Multimodal analysis

Explore and analyze multi-modal data in Seurat:

  • analyze CITE-seq data (RNA + protein)
  • compare expression and clustering across multiple assays

Stimulated vs Control PBMCs

An introduction to comparative analyses:

  • integrate across conditions
  • identify common cell types and markers
  • identify cell-type specific responses


Use the sctransform wrapper in Seurat

  • new method to remove technical variation while retaining biological heterogeneity

scATAC-seq + scRNA-seq integration

Integrate scRNA-seq data with scATAC-seq data

  • classify scATAC-seq cells based on scRNA-seq clusters
  • coembed scATAC-seq and scRNA-seq data

Frequently requested vignettes

Here we provide a series of short vignettes to demonstrate a number of features that are commonly used in Seurat. We’ve focused the vignettes around questions that we frequently receive from users by e-mail. Click on a vignette to get started.

Cell Cycle Regression

Mitigate the effects of cell cycle heterogeneity

  • compute cell cycle phase scores based on marker genes
  • regress out scores

Differential Expression Testing

Perform differential expression (DE) testing in Seurat

  • explore DE framework and results
  • speed up DE testing for large datasets

Demultiplex Cell Hashing data

Learn how to work with data produced with Cell Hashing:

  • demultiplex cells to sample of origin
  • identify cross sample doublets


Explore your data with many built in visualization options:

  • visualize cluster markers
  • Interact with plots and apply themes


Speed up compute-intensive functions with parallelization:

  • learn about the future framework
  • list of parallelized Seurat functions

Interoperability with Other Analysis Tools

Convert data between formats for different analysis tools:

  • Converters for SingleCellExperiment, anndata, and loom

Seurat Wrappers

In order to facilitate the use of community tools with Seurat, we provide the Seurat Wrappers package, which contains code to run other analysis tools on Seurat objects. For the initial release, we provide wrappers for a few packages in the table below but would encourage other package developers interested in interfacing with Seurat to check out our contributor guide here.

Package Vignette Reference Source
Conos Integration of datasets using Conos Barkas et al, Nature Methods 2019 https://github.com/hms-dbmi/conos
LIGER Integrating Seurat objects using LIGER Welch et al, Cell 2019 https://github.com/MacoskoLab/liger
fastMNN Running fastMNN on Seurat Objects Haghverdi et al, Nature Biotechnology 2018 https://bioconductor.org/packages/release/bioc/html/scran.html
Harmony Integration of datasets using Harmony Korsunsky et al, bioRxiv 2018 https://github.com/immunogenomics/harmony
ALRA Zero-preserving imputation with ALRA Linderman et al, bioRxiv 2018 https://github.com/KlugerLab/ALRA
Velocity Estimating RNA Velocity using Seurat La Manno et al, Nature 2018 https://velocyto.org
schex Using schex with Seurat Freytag, R package 2019 https://github.com/SaskiaFreytag/schex

Old Seurat Versions

Tutorials for Seurat versions 1.3-1.4 can be found here.
Tutorials for Seurat version <= 1.2 can be found here.
All current and previous versions of Seurat can be found on github.