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Seurat v3.1.4

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

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

3.1.4

License

GPL-3 | file LICENSE

Issues

349

Pull Requests

40

Stars

2,291

Forks

915

Maintainer

Paul Hoffman

Last Published

February 26th, 2020

Functions in Seurat (3.1.4)

Assay-class

The Assay Class
Assays

Pull Assays or assay names
BuildClusterTree

Phylogenetic Analysis of Identity Classes
BarcodeInflectionsPlot

Plot the Barcode Distribution and Calculated Inflection Points
AnchorSet-class

The AnchorSet Class
AddModuleScore

Calculate module scores for feature expression programs in single cells
AverageExpression

Averaged feature expression by identity class
ALRAChooseKPlot

ALRA Approximate Rank Selection Plot
AugmentPlot

Augments ggplot2-based plot with a PNG image.
AddMetaData

Add in metadata associated with either cells or features.
CellsByIdentities

Get cell names grouped by identity class
CalculateBarcodeInflections

Calculate the Barcode Distribution Inflection
CollapseSpeciesExpressionMatrix

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

Create an Assay object
CreateDimReducObject

Create a DimReduc object
Cells

Get cells present in an object
CellSelector

Cell selector
CombinePlots

Combine ggplot2-based plots into a single plot
CellScatter

Cell-cell scatter plot
Command

Get SeuratCommands
CellCycleScoring

Score cell cycle phases
ExpMean

Calculate the mean of logged values
CaseMatch

Match the case of character vectors
CreateGeneActivityMatrix

Convert a peak matrix to a gene activity matrix
DimHeatmap

Dimensional reduction heatmap
DimPlot

Dimensional reduction plot
CreateSeuratObject

Create a Seurat object
ColorDimSplit

Color dimensional reduction plot by tree split
FetchData

Access cellular data
DotPlot

Dot plot visualization
DoHeatmap

Feature expression heatmap
CollapseEmbeddingOutliers

Move outliers towards center on dimension reduction plot
BlackAndWhite

Create a custom color palette
CustomDistance

Run a custom distance function on an input data matrix
ExpVar

Calculate the variance of logged values
DietSeurat

Slim down a Seurat object
DefaultAssay

Get and set the default assay
FindAllMarkers

Gene expression markers for all identity classes
ElbowPlot

Quickly Pick Relevant Dimensions
Embeddings

Get cell embeddings
GetResidual

Calculate pearson residuals of features not in the scale.data
Graph-class

The Graph Class
ExportToCellbrowser

Export Seurat object for UCSC cell browser
DimReduc-class

The Dimmensional Reduction Class
Key

Get a key
HTOHeatmap

Hashtag oligo heatmap
JackStraw

Determine statistical significance of PCA scores.
JS

Get JackStraw information
HTODemux

Demultiplex samples based on data from cell 'hashing'
L2CCA

L2-Normalize CCA
Idents

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

Integrate data
LocalStruct

Calculate the local structure preservation metric
FindMarkers

Gene expression markers of identity classes
FindIntegrationAnchors

Find integration anchors
FindNeighbors

SNN Graph Construction
MetaFeature

Aggregate expression of multiple features into a single feature
MULTIseqDemux

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

Polygon FeaturePlot
LogNormalize

Normalize raw data
PolyDimPlot

Polygon DimPlot
ExpSD

Calculate the standard deviation of logged values
FeaturePlot

Visualize 'features' on a dimensional reduction plot
RenameCells

Rename cells
RenameAssays

Rename assays in a Seurat object
DiscretePalette

Discrete colour palettes from the pals package
Read10X_h5

Read 10X hdf5 file
ReadAlevin

Load in data from Alevin pipeline
FeatureScatter

Scatter plot of single cell data
MinMax

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

Access miscellaneous data
FindTransferAnchors

Find transfer anchors
FindConservedMarkers

Finds markers that are conserved between the groups
FindClusters

Cluster Determination
JackStrawData-class

The JackStrawData Class
GetAssayData

General accessor function for the Assay class
RunICA

Run Independent Component Analysis on gene expression
JackStrawPlot

JackStraw Plot
ScoreJackStraw

Compute Jackstraw scores significance.
RunLSI

Run Latent Semantic Indexing on binary count matrix
SelectIntegrationFeatures

Select integration features
LogSeuratCommand

Log a command
SetAssayData

Setter for multimodal data
as.Graph

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

Convert objects to Seurat objects
FindVariableFeatures

Find variable features
SeuratTheme

Seurat Themes
TopFeatures

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

Transfer Labels
SeuratCommand-class

The SeuratCommand Class
GetAssay

Get an Assay object from a given Seurat object.
IntegrationData-class

The IntegrationData Class
LogVMR

Calculate the variance to mean ratio of logged values
IsGlobal

Is an object global/persistent?
Loadings

Get feature loadings
LabelPoints

Add text labels to a ggplot2 plot
OldWhichCells

Identify cells matching certain criteria
ProjectDim

Project Dimensional reduction onto full dataset
Read10X

Load in data from 10X
PCASigGenes

Significant genes from a PCA
RunALRA

Run Adaptively-thresholded Low Rank Approximation (ALRA)
RunCCA

Perform Canonical Correlation Analysis
MixingMetric

Calculates a mixing metric
RunUMAP

Run UMAP
SetIntegrationData

Set integation data
HoverLocator

Hover Locator
HVFInfo

Get highly variable feature information
GetIntegrationData

Get integation data
L2Dim

L2-normalization
SCTransform

Use regularized negative binomial regression to normalize UMI count data
Seurat-package

Tools for single-cell genomics
Seurat-class

The Seurat Class
NormalizeData

Normalize Data
LabelClusters

Label clusters on a ggplot2-based scatter plot
ReadAlevinCsv

Load in data from Alevin pipeline
VizDimLoadings

Visualize Dimensional Reduction genes
VlnPlot

Single cell violin plot
as.SingleCellExperiment

Convert objects to SingleCellExperiment objects
Reductions

Pull DimReducs or DimReduc names
as.list.SeuratCommand

Coerce a SeuratCommand to a list
RegroupIdents

Regroup idents based on meta.data info
PrepSCTIntegration

Prepare an object list that has been run through SCTransform for integration
print.DimReduc

Print the results of a dimensional reduction analysis
RidgePlot

Single cell ridge plot
Project

Get and set project information
PercentageFeatureSet

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

Merge two matrices by rowname
PlotClusterTree

Plot clusters as a tree
RunPCA

Run Principal Component Analysis
[.Seurat

Subset a Seurat object
SubsetData

Return a subset of the Seurat object
RunTSNE

Run t-distributed Stochastic Neighbor Embedding
SplitObject

Splits object into a list of subsetted objects.
seurat-class

The Seurat Class
VariableFeaturePlot

View variable features
VariableFeatures

Get and set variable feature information
cc.genes.updated.2019

Cell cycle genes: 2019 update
TF.IDF

Term frequency-inverse document frequency
Tool

Get and set additional tool data
pbmc_small

A small example version of the PBMC dataset
cc.genes

Cell cycle genes
TopCells

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

Get the standard deviations for an object
WhichCells

Identify cells matching certain criteria
RelativeCounts

Normalize raw data to fractions
as.CellDataSet

Convert objects to CellDataSet objects
ReadH5AD

Read from and write to h5ad files
merge.Assay

Merge Seurat Objects
as.loom

Convert objects to loom objects
UpdateSeuratObject

Update old Seurat object to accomodate new features
ScaleData

Scale and center the data.
UpdateSymbolList

Get updated synonyms for gene symbols
StopCellbrowser

Stop Cellbrowser web server
SubsetByBarcodeInflections

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

Sample UMI
as.sparse

Convert between data frames and sparse matrices