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Seurat v4.0

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

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

47,332

Version

4.0.1

License

GPL-3 | file LICENSE

Issues

Pull Requests

Stars

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Repository

https://github.com/satijalab/seurat

Homepage

https://satijalab.org/seurat

Maintainer

Last Published

March 18th, 2021

Functions in Seurat (4.0.1)

Calculate module scores for feature expression programs in single cells

BuildClusterTree

Phylogenetic Analysis of Identity Classes

AverageExpression

Averaged feature expression by identity class

The Assay Class

Determine text color based on background color

AnchorSet-class

The AnchorSet Class

AggregateExpression

Aggregated feature expression by identity class

Augments ggplot2-based plot with a PNG image.

BarcodeInflectionsPlot

Plot the Barcode Distribution and Calculated Inflection Points

AnnotateAnchors

Add info to anchor matrix

The DimReduc Class

Dimensional reduction plot

Calculate a perturbation Signature

CalculateBarcodeInflections

Calculate the Barcode Distribution Inflection

Cluster Determination

CollapseEmbeddingOutliers

Move outliers towards center on dimension reduction plot

DiscretePalette

Discrete colour palettes from the pals package

CollapseSpeciesExpressionMatrix

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

Feature expression heatmap

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

Filter stray beads from Slide-seq puck

FindTransferAnchors

Find transfer anchors

FindConservedMarkers

Finds markers that are conserved between the groups

FindVariableFeatures

Find variable features

Demultiplex samples based on data from cell 'hashing'

CellCycleScoring

Score cell cycle phases

Run a custom distance function on an input data matrix

CreateSCTAssayObject

Create a SCT Assay object

Dot plot visualization

Match the case of character vectors

Cell-cell scatter plot

Create a custom color palette

FindIntegrationAnchors

Find integration anchors

Quickly Pick Relevant Dimensions

Gene expression markers of identity classes

Hashtag oligo heatmap

HVFInfo.SCTAssay

Get Variable Feature Information

Combine ggplot2-based plots into a single plot

Color dimensional reduction plot by tree split

Scale and/or center matrix rowwise

Calculate the variance of logged values

IntegrationAnchorSet-class

The IntegrationAnchorSet Class

IntegrateEmbeddings

Integrate low dimensional embeddings

Gene expression markers for all identity classes

Linear discriminant analysis on pooled CRISPR screen data.

Calculate the local structure preservation metric

Normalize raw data

FindSpatiallyVariableFeatures

Find spatially variable features

ModalityWeights-class

The ModalityWeights Class

PlotClusterTree

Plot clusters as a tree

PercentageFeatureSet

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

Aggregate expression of multiple features into a single feature

Get an Assay object from a given Seurat object.

Find subclusters under one cluster

DE and EnrichR pathway visualization barplot

L2-Normalize CCA

GetTissueCoordinates.SlideSeq

Get Tissue Coordinates

Determine statistical significance of PCA scores.

IntegrationData-class

The IntegrationData Class

ISpatialFeaturePlot

Visualize features spatially and interactively

Calculate pearson residuals of features not in the scale.data

Calculate the mean of logged values

L2-normalization

Load10X_Spatial

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

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

Map query cells to a reference

Load Slide-seq spatial data

Load in data from remote or local mtx files

FindMultiModalNeighbors

Construct weighted nearest neighbor graph

(Shared) Nearest-neighbor graph construction

Calculate the standard deviation of logged values

Slim down a Seurat object

Highlight Neighbors in DimPlot

Metric for evaluating mapping success

The Neighbor Class

PlotPerturbScore

Function to plot perturbation score distributions.

The Graph Class

GetTransferPredictions

Get the predicted identity

Function to prepare data for Linear Discriminant Analysis.

Visualize 'features' on a dimensional reduction plot

Dimensional reduction heatmap

Polygon DimPlot

ISpatialDimPlot

Visualize clusters spatially and interactively

Visualize features in dimensional reduction space interactively

Label clusters on a ggplot2-based scatter plot

Apply a ceiling and floor to all values in a matrix

Load the Annoy index file

Add text labels to a ggplot2 plot

Load STARmap data

Project query into UMAP coordinates of a reference

Project Dimensional reduction onto full dataset

Perform Canonical Correlation Analysis

Calculates a mixing metric

MixscapeHeatmap

Differential expression heatmap for mixscape

Run Independent Component Analysis on gene expression

The SCTModel Class

RenameCells.SCTAssay

Rename Cells in an Object

Run t-distributed Stochastic Neighbor Embedding

Regroup idents based on meta.data info

SpatialImage-class

The SpatialImage Class

Normalize raw data to fractions

The VisiumV1 class

Visualize spatial clustering and expression data.

Visualize Dimensional Reduction genes

Single cell ridge plot

Run Linear Discriminant Analysis

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

Get SCT results from an Assay

Seurat: Tools for Single Cell Genomics

PrepSCTIntegration

Prepare an object list normalized with sctransform for integration.

Radius.SlideSeq

Get Spot Radius

Run Principal Component Analysis

Use regularized negative binomial regression to normalize UMI count data

Load in data from 10X

SeuratCommand-class

The SeuratCommand Class

Get nearest neighbors for given cell

TransferAnchorSet-class

The TransferAnchorSet Class

Run Supervised Principal Component Analysis

The STARmap class

cc.genes.updated.2019

Cell cycle genes: 2019 update

Save the Annoy index

Scatter plot of single cell data

GetImage.SlideSeq

Splits object into a list of subsetted objects.

SubsetByBarcodeInflections

Subset a Seurat Object based on the Barcode Distribution Inflection Points

Compute Jackstraw scores significance.

SetIntegrationData

Set integration data

SelectIntegrationFeatures

Select integration features

Find cells with highest scores for a given dimensional reduction technique

GetIntegrationData

Get integration data

GroupCorrelation

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

The Seurat Class

Convert objects to CellDataSet objects

subset.AnchorSet

Subset an AnchorSet object

contrast-theory

Get the intensity and/or luminance of a color

Single cell violin plot

Find features with highest scores for a given dimensional reduction technique

GroupCorrelationPlot

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

The SlideSeq class

Significant genes from a PCA

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

Calculate the variance to mean ratio of logged values

JackStrawData-class

The JackStrawData Class

Predict value from nearest neighbors

PolyFeaturePlot

Polygon FeaturePlot

Load a 10X Genomics Visium Image

as.Seurat.CellDataSet

Convert objects to Seurat objects

as.SingleCellExperiment

Convert objects to SingleCellExperiment objects

VariableFeaturePlot

View variable features

UpdateSymbolList

Get updated synonyms for gene symbols

Read 10X hdf5 file

as.sparse.H5Group

Cell cycle genes

UpdateSCTAssays

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

Merge SCTAssay objects

Compute Moran's I value.

Get image scale factors

Scale and center the data.

Objects exported from other packages