We provide a series of vignettes, tutorials, and analysis walkthroughs to help users get started with Seurat. You can also check out our Reference page which contains a full list of functions available to users.

Introductory Vignettes

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

We provide an additional introductory vignette for users who are interested in analyzing multimodal single-cell datasets (e.g. from CITE-seq, or the 10x multiome kit).

A basic overview of Seurat that includes an introduction to common analytical workflows.An introduction to working with multi-modal datasets in Seurat.

Data Integration

Recently, we have developed computational methods for integrated analysis of single-cell datasets generated across different conditions, technologies, or species. As an example, we provide a guided walk through for integrating and comparing PBMC datasets generated under different stimulation conditions. We provide additional vignettes demonstrating how to leverage an annotated scRNA-seq reference to map and label cells from a query, and to efficiently integrate large datasets.

An introduction to integrating scRNA-seq datasets in order to identify and compare shared cell types across experiments.Learn how to map a query scRNA-seq dataset onto a reference in order to automate the annotation and visualization of query cells.Identify anchors using the reciprocal PCA (rPCA) workflow, which performs a faster and more conservative integration.
Tips and examples for integrating very large scRNA-seq datasets (including >200,000 cells).Annotate, visualize, and interpret an scATAC-seq experiment using scRNA-seq data from the same biological system.Analyze query data in the context of multimodal reference atlases.

Additional New Methods

Seurat also offers additional novel statistical methods for analyzing single-cell data. These include:

  • Weighted-nearest neighbor (WNN) analysis: to define cell state based on multiple modalities [paper]
  • Mixscape: to analyze data from pooled single-cell CRISPR screens [paper]
  • SCTransform: Improved normalization for single-cell RNA-seq data [paper]]
  • SCTransform, v2 regularization [paper]]
Analyze multimodal single-cell data with weighted nearest neighbor analysis in Seurat v4.Explore new methods to analyze pooled single-celled perturbation screens.Examples of how to use the SCTransform wrapper in Seurat.
Examples of how to perform normalization, feature selection, integration, and differential expression with an updated version of sctransform.


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. Click on a vignette to get started.

An overview of the major visualization functionality within Seurat.Mitigate the effects of cell cycle heterogeneity by computing cell cycle phase scores based on marker genes.Perform differential expression (DE) testing in Seurat using a number of frameworks.
Learn how to work with data produced with Cell Hashing.Convert data between formats for different analysis tools.Speed up compute-intensive functions with parallelization.


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.

alevinImport alevin counts into SeuratSrivastava et. al., Genome Biology 2019https://github.com/k3yavi/alevin-Rtools
ALRAZero-preserving imputation with ALRALinderman et al, bioRxiv 2018https://github.com/KlugerLab/ALRA
CoGAPSRunning CoGAPS on Seurat ObjectsStein-O’Brien et al, Cell Systems 2019https://www.bioconductor.org/packages/release/bioc/html/CoGAPS.html
ConosIntegration of datasets using ConosBarkas et al, Nature Methods 2019https://github.com/hms-dbmi/conos
fastMNNRunning fastMNN on Seurat ObjectsHaghverdi et al, Nature Biotechnology 2018https://bioconductor.org/packages/release/bioc/html/scran.html
glmpcaRunning GLM-PCA on a Seurat ObjectTownes et al, Genome Biology 2019https://github.com/willtownes/glmpca
HarmonyIntegration of datasets using HarmonyKorsunsky et al, Nature Methods 2019https://github.com/immunogenomics/harmony
LIGERIntegrating Seurat objects using LIGERWelch et al, Cell 2019https://github.com/MacoskoLab/liger
Monocle3Calculating Trajectories with Monocle 3 and SeuratCao et al, Nature 2019https://cole-trapnell-lab.github.io/monocle3
NebulosaVisualization of gene expression with NebulosaJose Alquicira-Hernandez and Joseph E. Powell, Under Reviewhttps://github.com/powellgenomicslab/Nebulosa
schexUsing schex with SeuratFreytag, R package 2019https://github.com/SaskiaFreytag/schex
scVeloEstimating RNA Velocity using Seurat and scVeloBergen et al, bioRxiv 2019https://scvelo.readthedocs.io/
VelocityEstimating RNA Velocity using SeuratLa Manno et al, Nature 2018https://velocyto.org
CIPRUsing CIPR with human PBMC dataEkiz et. al., BMC Bioinformatics 2020https://github.com/atakanekiz/CIPR-Package
miQCRunning miQC on Seurat objectsHippen et. al., bioRxiv 2021https://github.com/greenelab/miQC
tricycleRunning estimate_cycle_position from tricycle on Seurat ObjectsZheng et. al., bioRxiv 2021https://www.bioconductor.org/packages/release/bioc/html/tricycle.html