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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 seuratpackage@gmail.com 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)

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Version

Install

install.packages('Seurat')

Monthly Downloads

58,682

Version

1.4.0.16

License

GPL-3

Maintainer

Satija Lab

Last Published

January 24th, 2025

Functions in Seurat (1.4.0.16)

AssessNodes

Assess Internal Nodes
AssessSplit

Assess Cluster Split
BatchGene

Identify potential genes associated with batch effects
BuildClusterTree

Phylogenetic Analysis of Identity Classes
BuildRFClassifier

Build Random Forest Classifier
BuildSNN

SNN Graph Construction
AverageExpression

Averaged gene expression by identity class
AveragePCA

Average PCA scores by identity class
CellPlot

Cell-cell scatter plot
ClassifyCells

Classify New Data
FindAllMarkers

Gene expression markers for all identity classes
FindAllMarkersNode

Find all markers for a node
GenePlot

Scatter plot of single cell data
GetCentroids

Get cell centroids
AddImputedScore

Calculate imputed expression values
AddMetaData

Add Metadata
DBClustDimension

Perform spectral density clustering on single cells
DoHeatmap

Gene expression heatmap
DoKMeans

K-Means Clustering
HeatmapNode

Node Heatmap
ICA

Run Independent Component Analysis on gene expression
ClusterAlpha

Probability of detection by identity class
ColorTSNESplit

Color tSNE Plot Based on Split
DotPlot

Dot plot visualization
FeatureHeatmap

Vizualization of multiple features
AddSamples

Add samples into existing Seurat object.
AddSmoothedScore

Calculate smoothed expression values
DiffTTest

Differential expression testing using Student's t-test
DimPlot

Dimensional reduction plot
FeaturePlot

Visualize 'features' on a dimensional reduction plot
FetchData

Access cellular data
FindMarkersNode

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

Plot ICA map
ICHeatmap

Independent component heatmap
MakeSparse

Make object sparse
KClustDimension

Perform spectral k-means clustering on single cells
LogNormalize

Normalize raw data
PlotClusterTree

Plot phylogenetic tree
PlotNoiseModel

Visualize expression/dropout curve
MarkerTest

ROC-based marker discovery
PCElbowPlot

Quickly Pick Relevant PCs
PCHeatmap

Principal component heatmap
RefinedMapping

Quantitative refinement of spatial inferences
FitGeneK

Build mixture models of gene expression
MeanVarPlot

Identify variable genes
MergeNode

Merge subchilden of a node
PCAPlot

Plot PCA map
MergeSeurat

Merge Seurat Objects
NegBinomDETest

Negative binomial test for UMI-count based data
PCA

Run Principal Component Analysis on gene expression
PCAFast

Run Principal Component Analysis on gene expression using IRLBA
RunDiffusion

Run t-distributed Stochastic Neighbor Embedding
RunTSNE

Run t-distributed Stochastic Neighbor Embedding
StashIdent

Set identity class information
SubsetCells

Return a subset of the Seurat object
PCASigGenes

Significant genes from a PCA
PoissonDETest

Poisson test for UMI-count based data
PrintPCA

Print the results of a PCA analysis
DiffExpTest

Likelihood ratio test for zero-inflated data
FindClusters

Cluster Determination
FindMarkers

Gene expression markers of identity classes
ICTopGenes

Find genes with highest ICA scores
Setup

Setup Seurat object
Seurat-deprecated

Deprecated function(s) in the Seurat package
seurat

The Seurat Class
InitialMapping

Infer spatial origins for single cells
JackStraw

Determine statistical significance of PCA scores.
JackStrawPlot

JackStraw Plot
PCTopCells

Find cells with highest PCA scores
PCTopGenes

Find genes with highest PCA scores
ProjectPCA

Project Principal Components Analysis onto full dataset
Read10X

Load in data from 10X
SetAllIdent

Switch identity class definition to another variable
RegressOut

Regress out technical effects and cell cycle
SubsetData

Return a subset of the Seurat object
TSNEPlot

Plot tSNE map
VlnPlot

Single cell violin plot
RenameIdent

Rename one identity class to another
ReorderIdent

Reorder identity classes
SampleUMI

Sample UMI
ScaleData

Scale and center the data
VizICA

Visualize ICA genes
VizPCA

Visualize PCA genes
WhichCells

Identify cells matching certain criteria
TobitTest

Differential expression testing using Tobit models
ValidateClusters

Cluster Validation
SetIdent

Set identity class information
ValidateSpecificClusters

Specific Cluster Validation
VizClassification

Highlight classification results