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Seurat v5

Seurat is an R toolkit for single cell genomics, developed and maintained by the Satija Lab at NYGC.

We are excited to release Seurat v5! This updates introduces new functionality for spatial, multimodal, and scalable single-cell analysis.

Seurat v5 is backwards-compatible with previous versions, so that users will continue to be able to re-run existing workflows.

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 post on the github page with any questions or if you would like to contribute

For a version history/changelog, please see the NEWS file.

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Version

Install

install.packages('Seurat')

Monthly Downloads

39,699

Version

5.2.1

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/satijalab/seurat

Homepage

https://satijalab.org/seurat

Maintainer

Paul Hoffman

Last Published

January 24th, 2025

Functions in Seurat (5.2.1)

BarcodeInflectionsPlot

Plot the Barcode Distribution and Calculated Inflection Points

Calculate dispersion of features

Calculate a perturbation Signature

BuildNicheAssay

Construct an assay for spatial niche analysis

Match the case of character vectors

CellCycleScoring

Score cell cycle phases

Color dimensional reduction plot by tree split

Slim down a Seurat object

Combine ggplot2-based plots into a single plot

Find variable features based on dispersion

DE and EnrichR pathway visualization barplot

Create a custom color palette

CreateSCTAssayObject

Create a SCT Assay object

Cell-cell scatter plot

CalculateBarcodeInflections

Calculate the Barcode Distribution Inflection

Dimensional reduction heatmap

Quickly Pick Relevant Dimensions

Dimensional reduction plot

Calculate the mean of logged values

Scale and/or center matrix rowwise

FastRPCAIntegration

Perform integration on the joint PCA cell embeddings.

CollapseSpeciesExpressionMatrix

Slim down a multi-species expression matrix, when only one species is primarily of interenst.

Generate CountSketch random matrix

CollapseEmbeddingOutliers

Move outliers towards center on dimension reduction plot

The DimReduc Class

Feature expression heatmap

DiscretePalette

Discrete colour palettes from pals

Get a vector of cell names associated with an image (or set of images)

Gene expression markers for all identity classes

Run a custom distance function on an input data matrix

FindBridgeAnchor

Find bridge anchors between two unimodal datasets

FetchResidualSCTModel

Calculate pearson residuals of features not in the scale.data This function is the secondary function under FetchResiduals

Calculate pearson residuals of features not in the scale.data

Calculate the standard deviation of logged values

CreateCategoryMatrix

Create one hot matrix for a given label

Calculate the variance of logged values

Visualize 'features' on a dimensional reduction plot

Cluster Determination

FindVariableFeatures

Find variable features

FindTransferAnchors

Find transfer anchors

FindIntegrationAnchors

Find integration anchors

Gene expression markers of identity classes

FindBridgeIntegrationAnchors

Find integration bridge anchors between query and extended bridge-reference

Scatter plot of single cell data

FindBridgeTransferAnchors

Find bridge anchors between query and extended bridge-reference

Dot plot visualization

Filter stray beads from Slide-seq puck

The Graph Class

Get an Assay object from a given Seurat object.

FindSpatiallyVariableFeatures

Find spatially variable features

FindMultiModalNeighbors

Construct weighted nearest neighbor graph

FetchResiduals_reference

temporal function to get residuals from reference

GroupCorrelation

Compute the correlation of features broken down by groups with another covariate

FindConservedMarkers

Finds markers that are conserved between the groups

GetTransferPredictions

Get the predicted identity

GetTissueCoordinates.SlideSeq

Get Tissue Coordinates

(Shared) Nearest-neighbor graph construction

ISpatialFeaturePlot

Visualize features spatially and interactively

Gaussian sketching

Find subclusters under one cluster

HarmonyIntegration

Harmony Integration

Hashtag oligo heatmap

Spatial Cluster Plots

HVFInfo.SCTAssay

Get Variable Feature Information

GetImage.SlideSeq

GroupCorrelationPlot

Boxplot of correlation of a variable (e.g. number of UMIs) with expression data

Calculate pearson residuals of features not in the scale.data

L2-Normalize CCA

JointPCAIntegration

Seurat-Joint PCA Integration

GetIntegrationData

Get integration data

Demultiplex samples based on data from cell 'hashing'

IntegrationAnchorSet-class

The IntegrationAnchorSet Class

ImageFeaturePlot

Spatial Feature Plots

Visualize features in dimensional reduction space interactively

ISpatialDimPlot

Visualize clusters spatially and interactively

LoadCurioSeeker

Load Curio Seeker data

JackStrawData-class

The JackStrawData Class

IntegrateEmbeddings

Integrate low dimensional embeddings

Determine statistical significance of PCA scores.

Load STARmap data

IntegrationData-class

The IntegrationData Class

IntegrateLayers

Integrate Layers

L2-normalization

Leverage Score Calculation

Find variable features based on mean.var.plot

Normalize Raw Data

Add text labels to a ggplot2 plot

Label clusters on a ggplot2-based scatter plot

Calculate the local structure preservation metric

Load10X_Spatial

Load a 10x Genomics Visium Spatial Experiment into a Seurat object

Map query cells to a reference

Load the Annoy index file

Aggregate expression of multiple features into a single feature

Calculate the variance to mean ratio of logged values

Demultiplex samples based on classification method from MULTI-seq (McGinnis et al., bioRxiv 2018)

Metric for evaluating mapping success

Significant genes from a PCA

Linear discriminant analysis on pooled CRISPR screen data.

MixscapeHeatmap

Differential expression heatmap for mixscape

Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework

ModalityWeights-class

The ModalityWeights Class

Apply a ceiling and floor to all values in a matrix

Calculates a mixing metric

Highlight Neighbors in DimPlot

PrepareBridgeReference

Prepare the bridge and reference datasets

ProjectCellEmbeddings

Project query data to the reference dimensional reduction

Convert Neighbor class to an asymmetrical Graph class

The Neighbor Class

PrepSCTFindMarkers

Prepare object to run differential expression on SCT assay with multiple models

Predict value from nearest neighbors

Function to prepare data for Linear Discriminant Analysis.

Project query into UMAP coordinates of a reference

Read10X_probe_metadata

Read10x Probe Metadata

Read 10X hdf5 file

PolyFeaturePlot

Polygon FeaturePlot

RPCAIntegration

Seurat-RPCA Integration

Polygon DimPlot

Read output from Parse Biosciences

Read and Load Nanostring SMI data

Radius.SlideSeq

Get Spot Radius

PseudobulkExpression

Pseudobulk Expression

PrepSCTIntegration

Prepare an object list normalized with sctransform for integration.

Calculate the percentage of a vector above some threshold

Project full data to the sketch assay

Single cell ridge plot

Load in data from remote or local mtx files

Read and Load Akoya CODEX data

Run the mark variogram computation on a given position matrix and expression matrix.

Project Dimensional reduction onto full dataset

RunGraphLaplacian

Run Graph Laplacian Eigendecomposition

Run Independent Component Analysis on gene expression

Perform Canonical Correlation Analysis

ProjectIntegration

Integrate embeddings from the integrated sketched.assay

Read10X_ScaleFactors

Load 10X Genomics Visium Scale Factors

ProjectDimReduc

Project query data to reference dimensional reduction

Load a 10X Genomics Visium Image

PercentageFeatureSet

Calculate the percentage of all counts that belong to a given set of features

Normalize raw data to fractions

PlotClusterTree

Plot clusters as a tree

Perform sctransform-based normalization

The STARmap class

PlotPerturbScore

Function to plot perturbation score distributions.

Run t-distributed Stochastic Neighbor Embedding

Read Data From Vitessce

Read and Load MERFISH Input from Vizgen

Run Supervised Latent Semantic Indexing

RenameCells.SCTAssay

Rename Cells in an Object

Read10X_Coordinates

Load 10X Genomics Visium Tissue Positions

Compute Moran's I value.

Load in data from 10X

Seurat: Tools for Single Cell Genomics

The Seurat Class

A single correlation plot

Run Principal Component Analysis

Run Supervised Principal Component Analysis

Load Slide-seq spatial data

Plot a single expression by identity on a plot

Plot a single dimension

Read output from STARsolo

Scale and center the data.

A single heatmap from base R using image

Get SCT results from an Assay

The SCTModel Class

Compute Jackstraw scores significance.

Get image scale factors

SeuratCommand-class

The SeuratCommand Class

TransferSketchLabels

Transfer data from sketch data to full data

Regroup idents based on meta.data info

Run Linear Discriminant Analysis

SelectIntegrationFeatures

Select integration features

SingleImagePlot

Single Spatial Plot

Read and Load 10x Genomics Xenium in-situ data

The SlideSeq class

SelectIntegrationFeatures5

Select integration features

SelectSCTIntegrationFeatures

Select SCT integration features

Run Leiden clustering algorithm

SingleRasterMap

A single heatmap from ggplot2 using geom_raster

UpdateSCTAssays

Update pre-V4 Assays generated with SCTransform in the Seurat to the new SCTAssay class

Find the Quantile of Data

UpdateSymbolList

Get updated synonyms for gene symbols

as.SingleCellExperiment

Convert objects to SingleCellExperiment objects

Save the Annoy index

SetIntegrationData

Set integration data

SingleSpatialPlot

Base plotting function for all Spatial plots

as.sparse.H5Group

Visualize spatial clustering and expression data.

Splits object into a list of subsetted objects.

TransferAnchorSet-class

The TransferAnchorSet Class

SpatialImage-class

The SpatialImage Class

SubsetByBarcodeInflections

Subset a Seurat Object based on the Barcode Distribution Inflection Points

UnSketchEmbeddings

Transfer embeddings from sketched cells to the full data

Find features with highest scores for a given dimensional reduction technique

Convert objects to CellDataSet objects

Find cells with highest scores for a given dimensional reduction technique

Get nearest neighbors for given cell

Visualize Dimensional Reduction genes

as.Seurat.CellDataSet

Convert objects to Seurat objects

subset.AnchorSet

Subset an AnchorSet object

writing-integration

Writing Integration Method Functions

Single cell violin plot

cc.genes.updated.2019

Cell cycle genes: 2019 update

Cell cycle genes

The VisiumV1 class

The VisiumV2 class

VariableFeaturePlot

View variable features

Merge SCTAssay objects

Variance Stabilizing Transformation

contrast-theory

Get the intensity and/or luminance of a color

Objects exported from other packages

fortify-Spatial

Prepare Coordinates for Spatial Plots

Calculate module scores for feature expression programs in single cells

The Assay Class

AnchorSet-class

The AnchorSet Class

AggregateExpression

Aggregated feature expression by identity class

Automagically calculate a point size for ggplot2-based scatter plots

AddAzimuthResults

Add Azimuth Results

AddAzimuthScores

Add Azimuth Scores

AnnotateAnchors

Add info to anchor matrix

AverageExpression

Averaged feature expression by identity class

Augments ggplot2-based plot with a PNG image.

BridgeCellsRepresentation

Construct a dictionary representation for each unimodal dataset

BuildClusterTree

Phylogenetic Analysis of Identity Classes

BridgeReferenceSet-class

The BridgeReferenceSet Class The BridgeReferenceSet is an output from PrepareBridgeReference

Seurat-CCA Integration

Determine text color based on background color