Seurat v1.4.0


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Seurat : R toolkit for single cell genomics

Seurat : R toolkit for single cell genomics.


Build Status

Seurat v1.4

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 with any questions or if you would like to contribute

Version History

October 4, 2016

  • Version 1.4 released
  • Changes:
    • Improved tools for cluster evaluation/visualizations
    • Methods for combining and adding to datasets

August 22, 2016:

  • 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)

Functions in Seurat

Name Description
PrintPCA Print the results of a PCA analysis
ProjectPCA Project Principal Components Analysis onto full dataset
Setup Setup Seurat object
SetIdent Set identity class information
VizICA Visualize ICA genes
VizPCA Visualize PCA genes
DBClustDimension Perform spectral density clustering on single cells
BuildRFClassifier Build Random Forest Classifier
DiffExpTest Likelihood ratio test for zero-inflated data
GetCentroids Get cell centroids
BuildClusterTree Phylogenetic Analysis of Identity Classes
HeatmapNode Node Heatmap
AverageExpression Averaged gene expression by identity class
AveragePCA Average PCA scores by identity class
CellPlot Cell-cell scatter plot
ClassifyCells Classify New Data
DiffTTest Differential expression testing using Student's t-test
DimPlot Dimensional reduction plot
FindAllMarkers Gene expression markers for all identity classes
FindAllMarkersNode Find all markers for a node
ICHeatmap Independent component heatmap
ICTopGenes Find genes with highest ICA scores
JackStraw Determine statistical significance of PCA scores.
PCA Run Principal Component Analysis on gene expression
PCTopCells Find cells with highest PCA scores
PCTopGenes Find genes with highest PCA scores
RegressOut Regress out technical effects and cell cycle
RenameIdent Rename one identity class to another
AddImputedScore Calculate imputed expression values
BatchGene Identify potential genes associated with batch effects
AddSamples Add samples into existing Seurat object.
AddSmoothedScore Calculate smoothed expression values
DoHeatmap Gene expression heatmap
GenePlot Scatter plot of single cell data
InitialMapping Infer spatial origins for single cells
KClustDimension Perform spectral k-means clustering on single cells
LogNormalize Normalize raw data
ICA Run Independent Component Analysis on gene expression
MeanVarPlot Identify variable genes
MergeNode Merge subchilden of a node
RunTSNE Run t-distributed Stochastic Neighbor Embedding
SampleUMI Sample UMI
seurat The Seurat Class
StashIdent Set identity class information
NegBinomDETest Negative binomial test for UMI-count based data
MergeSeurat Merge Seurat Objects
ValidateSpecificClusters Specific Cluster Validation
PlotClusterTree Plot phylogenetic tree
VizClassification Highlight classification results
AssessSplit Assess Cluster Split
AssessNodes Assess Internal Nodes
PlotNoiseModel Visualize expression/dropout curve
Seurat-deprecated Deprecated function(s) in the Seurat package
ClusterAlpha Probability of detection by identity class
ColorTSNESplit Color tSNE Plot Based on Split
FindMarkers Gene expression markers of identity classes
FindClusters Cluster Determination
Read10X Load in data from 10X
RefinedMapping Quantitative refinement of spatial inferences
BuildSNN SNN Graph Construction
FindMarkersNode Gene expression markers of identity classes defined by a phylogenetic clade
WhichCells Identify cells matching certain criteria
AddMetaData Add Metadata
FitGeneK Build mixture models of gene expression
JackStrawPlot JackStraw Plot
PCASigGenes Significant genes from a PCA
PCElbowPlot Quickly Pick Relevant PCs
FeaturePlot Visualize 'features' on a dimensional reduction plot
FetchData Access cellular data
MarkerTest ROC-based marker discovery
MakeSparse Make object sparse Converts stored data matrices to sparse matrices to save space
PCHeatmap Principal component heatmap
ReorderIdent Reorder identity classes
RunDiffusion Run t-distributed Stochastic Neighbor Embedding
SubsetData Return a subset of the Seurat object
TobitTest Differential expression testing using Tobit models
ValidateClusters Cluster Validation
TSNEPlot Plot tSNE map
VlnPlot Single cell violin plot
ICAPlot Plot ICA map
DotPlot Dot plot visualization
FeatureHeatmap Vizualization of multiple features
DoKMeans K-Means Clustering
SubsetCells Return a subset of the Seurat object
PoissonDETest Negative binomial test for UMI-count based data
ScaleData Scale and center the data
SetAllIdent Switch identity class definition to another variable
PCAFast Run Principal Component Analysis on gene expression using IRLBA
PCAPlot Plot PCA map
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Last month downloads


Date 05/21/2015
SystemRequirements Java (>= 1.6)
LinkingTo Rcpp, RcppEigen, RcppProgress
License GPL-3
Collate 'RcppExports.R' 'seurat.R' 'cluster_determination.R' 'cluster_validation.R' 'deprecated_functions.R' 'seuratFxns.R' 'snn.R' 'tSNE_project.R' 'zfRenderSeurat.R'
RoxygenNote 5.0.1

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