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Seurat v1.3

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:

Seurat is also hosted on GitHub, you can view and clone the repository at

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 contact seuratpackage@gmail.com with any questions or if you would like to contribute

Version History

August 22, 2015:

  • Version 1.3 released
  • Changes :
    • Improved clustering approach - see FAQ for details
    • All functions support sparse matrices
    • Methods for removing unwanted sources of variation
    • Consistent function names
    • Updated visualizations

May 21, 2015:

  • Drop-Seq manuscript published. Version 1.2 released
  • Changes :
    • Added support for spectral t-SNE and density clustering
    • New visualizations - including pcHeatmap, dot.plot, and feature.plot
    • Expanded package documentation, reduced import package burden
    • Seurat code is now hosted on GitHub, enables easy install through devtools
    • Small bug fixes

April 13, 2015:

  • Spatial mapping manuscript published. Version 1.1 released (initial release)

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Version

Install

install.packages('Seurat')

Monthly Downloads

58,682

Version

1.3

License

GPL-3

Maintainer

Satija Lab

Last Published

January 24th, 2025

Functions in Seurat (1.3)

AddSamples

Add samples into existing Seurat object.
AddSmoothedScore

Calculate smoothed expression values
BuildClusterTree

Phylogenetic Analysis of Identity Classes
BuildSNN

SNN Graph Construction
BatchGene

Identify potential genes associated with batch effects
CellPlot

Cell-cell scatter plot
AverageExpression

Averaged gene expression by identity class
AveragePCA

Average PCA scores by identity class
AddMetaData

Add Metadata
AddImputedScore

Calculate imputed expression values
DiffTTest

Differential expression testing using Student's t-test
DiffExpTest

Likelihood ratio test for zero-inflated data
DoHeatmap

Gene expression heatmap
DBClustDimension

Perform spectral density clustering on single cells
DimPlot

Dimensional reduction plot
FeatureHeatmap

Vizualization of multiple features
ClusterAlpha

Probability of detection by identity class
FeaturePlot

Visualize 'features' on a dimensional reduction plot
DoKMeans

K-Means Clustering
DotPlot

Dot plot visualization
FindAllMarkers

Gene expression markers for all identity classes
FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade
GenePlot

Scatter plot of single cell data
ICA

Run Independent Component Analysis on gene expression
FitGeneK

Build mixture models of gene expression
FindClusters

Cluster Determination
GetCentroids

Get cell centroids
FetchData

Access cellular data
ICAPlot

Plot ICA map
FindMarkers

Gene expression markers of identity classes
ICHeatmap

Independent component heatmap
LogNormalize

Normalize raw data
MeanVarPlot

Identify variable genes
JackStrawPlot

JackStraw Plot
MarkerTest

ROC-based marker discovery
PCA

Run Principal Component Analysis on gene expression
InitialMapping

Infer spatial origins for single cells
JackStraw

Determine statistical significance of PCA scores.
KClustDimension

Perform spectral k-means clustering on single cells
ICTopGenes

Find genes with highest ICA scores
PlotClusterTree

Plot phylogenetic tree
PrintPCA

Print the results of a PCA analysis
PCTopGenes

Find genes with highest PCA scores
PlotNoiseModel

Visualize expression/dropout curve
PCTopCells

Find cells with highest PCA scores
PCHeatmap

Principal component heatmap
RunTSNE

Run t-distributed Stochastic Neighbor Embedding
RunDiffusion

Run t-distributed Stochastic Neighbor Embedding
TobitTest

Differential expression testing using Tobit models
SubsetData

Return a subset of the Seurat object
ScaleData

Scale and center the data
RegressOut

Regress out technical effects and cell cycle
SubsetCells

Return a subset of the Seurat object
RefinedMapping

Quantitative refinement of spatial inferences
StashIdent

Set identity class information
SetAllIdent

Switch identity class definition to another variable
VizPCA

Visualize PCA genes
ValidateClusters

Cluster Validation
TSNEPlot

Plot tSNE map
VlnPlot

Single cell violin plot
PCASigGenes

Significant genes from a PCA
SetIdent

Set identity class information
Read10X

Load in data from 10X
ProjectPCA

Project Principal Components Analysis onto full dataset
PCElbowPlot

Quickly Pick Relevant PCs
PCAFast

Run Principal Component Analysis on gene expression using IRLBA
VizICA

Visualize ICA genes
Setup

Setup Seurat object
ValidateSpecificClusters

Specific Cluster Validation
PCAPlot

Plot PCA map
RenameIdent

Rename one identity class to another
seurat

The Seurat Class
WhichCells

Identify matching cells
ReorderIdent

Reorder identity classes
Seurat-deprecated

Deprecated function(s) in the Seurat package