PBMC 3K guided tutorial
A basic overview of Seurat that includes an introduction to common analytical workflows.
GO: PBMC 3K guided tutorialThese tutorials and analysis walkthroughs introduce common workflows in Seurat.
- You can refer to the documentation for Seurat functions here.
- For vignettes from Seurat <= v4, see the archive.
Core workflows
For new users, we suggest starting with a guided walkthrough 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.
Introduction to multimodal analysis
An introduction to working with multimodal datasets in Seurat.
GO: Introduction to multimodal analysisData visualization
An overview of the visualization capabilities within Seurat.
GO: Data visualizationNormalization with SCTransform
Normalize scRNA-seq data and identify variable features with SCTransform.
GO: Normalization with SCTransformEssential commands
Reference list of commonly used commands to store, access, explore, and analyze datasets.
GO: Essential commandsSeurat v6
We highlight the new features and improvements introduced in Seurat v6.
Sample-level analysis with scSLIDE
Analyze differences between samples with scSLIDE using in-memory COVID-19 data and a large on-disk Alzheimer's disease dataset.
GO: Sample-level analysis with scSLIDEAnalyzing perturbation responses with RNA fingerprinting
Learn perturbation fingerprints, map query cells, and use a precomputed Genome-Wide Perturb-seq reference.
GO: Analyzing perturbation responses with RNA fingerprintingCell type annotation with Pan-human Azimuth
Annotate cell types with Pan-human Azimuth and explore predictions, confidence scores, and embeddings.
GO: Cell type annotation with Pan-human AzimuthData integration
We provide methods for integrated analysis of single-cell datasets generated across different conditions, technologies, or species.
Introduction to scRNA-seq integration
An introduction to integrating scRNA-seq datasets in order to identify and compare shared cell types across experiments.
GO: Introduction to scRNA-seq integrationComparing scRNA-seq integration methods
Integrate scRNA-seq datasets using a variety of computational methods.
GO: Comparing scRNA-seq integration methodsReference mapping and cell type annotation
Learn how to map a query scRNA-seq dataset onto a reference in order to automate the annotation and visualization of query cells.
GO: Reference mapping and cell type annotationMultimodal analysis
We also implement a suite of statistical methods for analyzing multimodal single-cell data. These include methods to integrate modalities that are simultaneously measured in the same cells, modalities that are measured in different cells, and techniques to analyze pooled CRISPR screens.
Bridge integration with dictionary learning
Map scATAC-seq onto an scRNA-seq reference using a multi-omic bridge dataset in Seurat v5.
GO: Bridge integration with dictionary learningWeighted nearest neighbor analysis
Analyze multimodal single-cell data with weighted nearest neighbor analysis.
GO: Weighted nearest neighbor analysisIntegrating scRNA-seq and scATAC-seq data
Annotate, visualize, and interpret an scATAC-seq experiment using scRNA-seq data from the same biological system.
GO: Integrating scRNA-seq and scATAC-seq dataMultimodal reference mapping
Analyze query data in the context of multimodal reference atlases.
GO: Multimodal reference mappingPerturbation analysis with Mixscape
Explore new methods to analyze pooled single-cell perturbation screens.
GO: Perturbation analysis with MixscapeLarge datasets
Seurat supports analysis of datasets with millions of cells without loading the full datasets into memory. Sketch-based workflows analyze a representative subset of cells in memory, while the full dataset remains on disk. The BPCells R package (Greenleaf Lab) provides efficient storage and analysis of on-disk data.
The vignettes below demonstrate sketch-based clustering and integration, reference mapping, and working with BPCells.
Sketch-based analysis
Analyze a 1.3 million cell mouse brain dataset using on-disk capabilities powered by BPCells.
GO: Sketch-based analysisSketch integration of 1 million cells
Perform sketch integration on a large dataset from Parse Biosciences.
GO: Sketch integration of 1 million cellsMapping COVID-19 PBMCs to a healthy reference
Map PBMC datasets from COVID-19 patients to a healthy PBMC reference.
GO: Mapping COVID-19 PBMCs to a healthy referenceOn-disk analysis with BPCells
Load and save large on-disk matrices using BPCells.
GO: On-disk analysis with BPCellsSpatial analysis
We provide support for the analysis of data from different spatial technologies, including sequencing-based and imaging-based methods.
Imaging-based spatial analysis
Learn to explore spatially-resolved data from multiplexed imaging technologies, including MERSCOPE, Xenium, CosMx SMI, and CODEX.
GO: Imaging-based spatial analysisSequencing-based spatial analysis
Learn to explore spatially-resolved transcriptomic data with examples from 10x Visium and Slide-seq v2.
GO: Sequencing-based spatial analysisVisium HD analysis (binned data)
Learn to explore spatially-resolved transcriptomic data in high-definition from 10x Visium HD.
GO: Visium HD analysis (binned data)Visium HD analysis (cell segmentations)
Learn to explore spatially-resolved transcriptomic data at the single-cell level from 10x Visium HD.
GO: Visium HD analysis (cell segmentations)Loading and visualizing 10x Atera data
Load and visualize 10x Atera data in Seurat.
GO: Loading and visualizing 10x Atera dataAdditional workflows
The vignettes below feature other commonly used functionality in Seurat, addressing questions that we frequently receive from users.
Cell-cycle scoring and regression
Mitigate the effects of cell cycle heterogeneity by computing cell cycle phase scores based on marker genes.
GO: Cell-cycle scoring and regressionDifferential expression and pseudobulk analysis
Perform differential expression (DE) testing in Seurat using a number of frameworks.
GO: Differential expression and pseudobulk analysisDemultiplexing with cell hashing
Learn how to work with data produced with Cell Hashing.
GO: Demultiplexing with cell hashingSeuratWrappers
In order to facilitate the use of community tools with Seurat, we provide the SeuratWrappers package, which contains code to run other analysis tools on Seurat objects. We encourage package developers interested in interfacing with Seurat to check out our contributor guide here.