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Seurat v3.1.1

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 20, 2019

  • Version 3.1
  • Changes:
    • Support for SCTransform integration workflows
    • Integration speed ups: reference-based integration + reciprocal PCA

April 12, 2019

  • Version 3.0
  • Changes:
    • Preprint published describing new methods for identifying anchors across single-cell datasets
    • Restructured Seurat object with native support for multimodal data
    • Parallelization support via future

July 20, 2018

  • Version 2.4
  • Changes:
    • Java dependency removed and functionality rewritten in Rcpp

March 22, 2018

  • Version 2.3
  • Changes:
    • New utility functions
    • Speed and efficiency improvments

January 10, 2018

  • Version 2.2
  • Changes:
    • Support for multiple-dataset alignment with RunMultiCCA and AlignSubspace
    • New methods for evaluating alignment performance

October 12, 2017

  • Version 2.1
  • Changes:
    • Support for using MAST and DESeq2 packages for differential expression testing in FindMarkers
    • Support for multi-modal single-cell data via @assay slot

July 26, 2017

  • Version 2.0
  • Changes:
    • Preprint released for integrated analysis of scRNA-seq across conditions, technologies and species
    • Significant restructuring of code to support clarity and dataset exploration
    • Methods for scoring gene expression and cell-cycle phase

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

49,115

Version

3.1.1

License

GPL-3 | file LICENSE

Issues

348

Pull Requests

40

Stars

2,289

Forks

914

Maintainer

Paul Hoffman

Last Published

May 10th, 2024

Functions in Seurat (3.1.1)

CalculateBarcodeInflections

Calculate the Barcode Distribution Inflection
CollapseSpeciesExpressionMatrix

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

Convert a peak matrix to a gene activity matrix
FindMarkers

Gene expression markers of identity classes
CreateDimReducObject

Create a DimReduc object
FindIntegrationAnchors

Find integration anchors
ExpSD

Calculate the standard deviation of logged values
CollapseEmbeddingOutliers

Move outliers towards center on dimension reduction plot
HVFInfo

Get highly variable feature information
ExpMean

Calculate the mean of logged values
CreateSeuratObject

Create a Seurat object
CaseMatch

Match the case of character vectors
AnchorSet-class

The AnchorSet Class
Command

Get SeuratCommands
Cells

Get cells present in an object
CreateAssayObject

Create an Assay object
DimPlot

Dimensional reduction plot
ElbowPlot

Quickly Pick Relevant Dimensions
CustomDistance

Run a custom distance function on an input data matrix
CellSelector

Cell selector
Embeddings

Get cell embeddings
DimReduc-class

The Dimmensional Reduction Class
FindConservedMarkers

Finds markers that are conserved between the groups
FindClusters

Cluster Determination
GetResidual

Calculate pearson residuals of features not in the scale.data
ExpVar

Calculate the variance of logged values
GetIntegrationData

Get integation data
ExportToCellbrowser

Export Seurat object for UCSC cell browser
FindTransferAnchors

Find transfer anchors
FindNeighbors

SNN Graph Construction
GetAssayData

General accessor function for the Assay class
Graph-class

The Graph Class
HoverLocator

Hover Locator
CellCycleScoring

Score cell cycle phases
ColorDimSplit

Color dimensional reduction plot by tree split
CellScatter

Cell-cell scatter plot
DotPlot

Dot plot visualization
DimHeatmap

Dimensional reduction heatmap
DoHeatmap

Feature expression heatmap
FeaturePlot

Visualize 'features' on a dimensional reduction plot
DietSeurat

Slim down a Seurat object
FeatureScatter

Scatter plot of single cell data
CombinePlots

Combine ggplot2-based plots into a single plot
JackStraw

Determine statistical significance of PCA scores.
JackStrawData-class

The JackStrawData Class
HTODemux

Demultiplex samples based on data from cell 'hashing'
MapQuery

Map queries to reference
MetaFeature

Aggregate expression of multiple features into a single feature
PolyFeaturePlot

Polygon FeaturePlot
HTOHeatmap

Hashtag oligo heatmap
RidgePlot

Single cell ridge plot
RunALRA

Run Adaptively-thresholded Low Rank Approximation (ALRA)
PrepSCTIntegration

Prepare an object list that has been run through SCTransform for integration
LabelClusters

Label clusters on a ggplot2-based scatter plot
FetchData

Access cellular data
Idents

Get, set, and manipulate an object's identity classes
LogVMR

Calculate the variance to mean ratio of logged values
IntegrationData-class

The IntegrationData Class
JS

Get JackStraw information
SeuratTheme

Seurat Themes
IntegrateData

Integrate data
LogSeuratCommand

Log a command
LogNormalize

Normalize raw data
MULTIseqDemux

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

Pairwise dataset integration
BlackAndWhite

Create a custom color palette
PlotClusterTree

Plot clusters as a tree
LabelPoints

Add text labels to a ggplot2 plot
PolyDimPlot

Polygon DimPlot
L2CCA

L2-Normalize CCA
FindAllMarkers

Gene expression markers for all identity classes
L2Dim

L2-normalization
SplitObject

Splits object into a list of subsetted objects.
PercentageFeatureSet

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

Calculates a mixing metric
Project

Get and set project information
ProjectDim

Project Dimensional reduction onto full dataset
LocalStruct

Calculate the local structure preservation metric
Loadings

Get feature loadings
OldWhichCells

Identify cells matching certain criteria
SCTransform

Use regularized negative binomial regression to normalize UMI count data
RunTSNE

Run t-distributed Stochastic Neighbor Embedding
PCASigGenes

Significant genes from a PCA
RunUMAP

Run UMAP
SampleUMI

Sample UMI
DefaultAssay

Get and set the default assay
SubsetByBarcodeInflections

Subset a Seurat Object based on the Barcode Distribution Inflection Points
ReadAlevinCsv

Load in data from Alevin pipeline
Read10X_h5

Read 10X hdf5 file
Read10X

Load in data from 10X
NormalizeData

Normalize Data
ReadAlevin

Load in data from Alevin pipeline
SubsetData

Return a subset of the Seurat object
VlnPlot

Single cell violin plot
RunCCA

Perform Canonical Correlation Analysis
RunPCA

Run Principal Component Analysis
RunICA

Run Independent Component Analysis on gene expression
RunLSI

Run Latent Semantic Indexing on binary count matrix
SetIntegrationData

Set integation data
FindVariableFeatures

Find variable features
Seurat-class

The Seurat Class
MinMax

Apply a ceiling and floor to all values in a matrix
GetAssay

Get an Assay object from a given Seurat object.
Key

Get a key
JackStrawPlot

JackStraw Plot
Stdev

Get the standard deviations for an object
Misc

Access miscellaneous data
Reductions

Pull DimReducs or DimReduc names
RegroupIdents

Regroup idents based on meta.data info
ScaleData

Scale and center the data.
ScoreJackStraw

Compute Jackstraw scores significance.
RelativeCounts

Normalize raw data to fractions
WhichCells

Identify cells matching certain criteria
as.list.SeuratCommand

Coerce a SeuratCommand to a list
RenameCells

Rename cells
SelectIntegrationFeatures

Select integration features
as.CellDataSet

Convert objects to CellDataSet objects
SetAssayData

Setter for multimodal data
TopCells

Find cells with highest scores for a given dimensional reduction technique
StopCellbrowser

Stop Cellbrowser web server
as.Graph

Convert a matrix (or Matrix) to the Graph class.
TransferData

Transfer Labels
SeuratCommand-class

The SeuratCommand Class
TF.IDF

Term frequency-inverse document frequency
cc.genes.updated.2019

Cell cycle genes: 2019 update
VariableFeaturePlot

View variable features
Seurat-package

Tools for single-cell genomics
UpdateSymbolList

Get updated synonyms for gene symbols
Tool

Get and set additional tool data
as.sparse

Convert between data frames and sparse matrices
as.loom

Convert objects to loom objects
pbmc_small

A small example version of the PBMC dataset
cc.genes

Cell cycle genes
ReadH5AD

Read from and write to h5ad files
TopFeatures

Find features with highest scores for a given dimensional reduction technique
VariableFeatures

Get and set variable feature information
seurat-class

The Seurat Class
merge.Assay

Merge Seurat Objects
VizDimLoadings

Visualize Dimensional Reduction genes
as.SingleCellExperiment

Convert objects to SingleCellExperiment objects
as.Seurat

Convert objects to Seurat objects
UpdateSeuratObject

Update old Seurat object to accomodate new features
[.Seurat

Subset a Seurat object
print.DimReduc

Print the results of a dimensional reduction analysis
AddModuleScore

Calculate module scores for feature expression programs in single cells
Assay-class

The Assay Class
AverageExpression

Averaged feature expression by identity class
AugmentPlot

Augments ggplot2-based plot with a PNG image.
Assays

Pull Assays or assay names
BarcodeInflectionsPlot

Plot the Barcode Distribution and Calculated Inflection Points
BuildClusterTree

Phylogenetic Analysis of Identity Classes
ALRAChooseKPlot

ALRA Approximate Rank Selection Plot
AddMetaData

Add in metadata associated with either cells or features.