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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

60,896

Version

4.0.4

License

GPL-3 | file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/satijalab/seurat

Homepage

https://satijalab.org/seurat

Maintainer

Paul Hoffman

Last Published

August 20th, 2021

Functions in Seurat (4.0.4)

CollapseEmbeddingOutliers

Move outliers towards center on dimension reduction plot

CollapseSpeciesExpressionMatrix

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

Slim down a Seurat object

Dimensional reduction heatmap

CalculateBarcodeInflections

Calculate the Barcode Distribution Inflection

Augments ggplot2-based plot with a PNG image.

The Assay Class

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

Dot plot visualization

AnchorSet-class

The AnchorSet Class

AnnotateAnchors

Add info to anchor matrix

Calculate the standard deviation of logged values

Calculate a perturbation Signature

Calculate the mean of logged values

Dimensional reduction plot

GetTransferPredictions

Get the predicted identity

FindConservedMarkers

Finds markers that are conserved between the groups

Cluster Determination

(Shared) Nearest-neighbor graph construction

FindTransferAnchors

Find transfer anchors

AddAzimuthScores

Add Azimuth Scores

AddAzimuthResults

Add Azimuth Results

FindVariableFeatures

Find variable features

IntegrateEmbeddings

Integrate low dimensional embeddings

ModalityWeights-class

The ModalityWeights Class

IntegrationAnchorSet-class

The IntegrationAnchorSet Class

Run Independent Component Analysis on gene expression

Calculate module scores for feature expression programs in single cells

Label clusters on a ggplot2-based scatter plot

Map query cells to a reference

Quickly Pick Relevant Dimensions

GetTissueCoordinates.SlideSeq

Get Tissue Coordinates

AggregateExpression

Aggregated feature expression by identity class

Cell-cell scatter plot

Feature expression heatmap

CreateSCTAssayObject

Create a SCT Assay object

Determine statistical significance of PCA scores.

FindSpatiallyVariableFeatures

Find spatially variable features

Run a custom distance function on an input data matrix

PrepSCTIntegration

Prepare an object list normalized with sctransform for integration.

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

Significant genes from a PCA

Calculate the variance to mean ratio of logged values

FindMultiModalNeighbors

Construct weighted nearest neighbor graph

Load STARmap data

BarcodeInflectionsPlot

Plot the Barcode Distribution and Calculated Inflection Points

Filter stray beads from Slide-seq puck

Create a custom color palette

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Gene expression markers for all identity classes

Color dimensional reduction plot by tree split

DiscretePalette

Discrete colour palettes from the pals package

Combine ggplot2-based plots into a single plot

HVFInfo.SCTAssay

Get Variable Feature Information

Run Linear Discriminant Analysis

Load10X_Spatial

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

Calculate the variance of logged values

Add text labels to a ggplot2 plot

Visualize features in dimensional reduction space interactively

Read 10X hdf5 file

DE and EnrichR pathway visualization barplot

Scale and/or center matrix rowwise

Project query into UMAP coordinates of a reference

Calculate pearson residuals of features not in the scale.data

The DimReduc Class

Convert objects to CellDataSet objects

Load a 10X Genomics Visium Image

Regroup idents based on meta.data info

ISpatialDimPlot

Visualize clusters spatially and interactively

ISpatialFeaturePlot

Visualize features spatially and interactively

Normalize raw data to fractions

SelectIntegrationFeatures

Select integration features

Linear discriminant analysis on pooled CRISPR screen data.

Visualize 'features' on a dimensional reduction plot

Metric for evaluating mapping success

Demultiplex samples based on data from cell 'hashing'

JackStrawData-class

The JackStrawData Class

Seurat: Tools for Single Cell Genomics

Single cell violin plot

Get nearest neighbors for given cell

TransferAnchorSet-class

The TransferAnchorSet Class

Compute Jackstraw scores significance.

Normalize raw data

Load in data from remote or local mtx files

Scatter plot of single cell data

FindIntegrationAnchors

Find integration anchors

IntegrationData-class

The IntegrationData Class

GetImage.SlideSeq

Gene expression markers of identity classes

PolyFeaturePlot

Polygon FeaturePlot

Get SCT results from an Assay

Predict value from nearest neighbors

Find subclusters under one cluster

The Graph Class

Apply a ceiling and floor to all values in a matrix

Single cell ridge plot

The SCTModel Class

Load the Annoy index file

Get an Assay object from a given Seurat object.

Calculate the local structure preservation metric

Project Dimensional reduction onto full dataset

PercentageFeatureSet

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

Hashtag oligo heatmap

Merge SCTAssay objects

Radius.SlideSeq

Get Spot Radius

Aggregate expression of multiple features into a single feature

RenameCells.SCTAssay

Rename Cells in an Object

Load in data from 10X

Function to prepare data for Linear Discriminant Analysis.

Run Supervised Principal Component Analysis

PlotClusterTree

Plot clusters as a tree

Splits object into a list of subsetted objects.

Calculates a mixing metric

SubsetByBarcodeInflections

Subset a Seurat Object based on the Barcode Distribution Inflection Points

The VisiumV1 class

Run Principal Component Analysis

Run t-distributed Stochastic Neighbor Embedding

Perform Canonical Correlation Analysis

Visualize Dimensional Reduction genes

subset.AnchorSet

Subset an AnchorSet object

as.sparse.H5Group

GroupCorrelationPlot

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

Read output from Parse Biosciences

GroupCorrelation

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

GetIntegrationData

Get integration data

The Seurat Class

SpatialImage-class

The SpatialImage Class

Objects exported from other packages

Load Slide-seq spatial data

UpdateSymbolList

Get updated synonyms for gene symbols

SeuratCommand-class

The SeuratCommand Class

MixscapeHeatmap

Differential expression heatmap for mixscape

L2-Normalize CCA

cc.genes.updated.2019

Cell cycle genes: 2019 update

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

L2-normalization

Highlight Neighbors in DimPlot

VariableFeaturePlot

View variable features

The Neighbor Class

Polygon DimPlot

SetIntegrationData

Set integration data

Visualize spatial clustering and expression data.

Compute Moran's I value.

The SlideSeq class

Save the Annoy index

PlotPerturbScore

Function to plot perturbation score distributions.

Find cells with highest scores for a given dimensional reduction technique

Read output from STARsolo

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

UpdateSCTAssays

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

Cell cycle genes

Use regularized negative binomial regression to normalize UMI count data

Scale and center the data.

as.SingleCellExperiment

Convert objects to SingleCellExperiment objects

Find features with highest scores for a given dimensional reduction technique

The STARmap class

Get image scale factors

contrast-theory

Get the intensity and/or luminance of a color

as.Seurat.CellDataSet

Convert objects to Seurat objects

Determine text color based on background color

AverageExpression

Averaged feature expression by identity class

Match the case of character vectors

CellCycleScoring

Score cell cycle phases