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Seurat v2.3

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:

http://www.satijalab.org/seurat

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

https://github.com/satijalab/seurat

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

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

60,896

Version

2.3.0

License

GPL-3 | file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/satijalab/seurat

Homepage

http://www.satijalab.org/seurat

Maintainer

Satija Lab

Last Published

January 24th, 2025

Functions in Seurat (2.3.0)

AddImputedScore

Calculate imputed expression values

Add samples into existing Seurat object.

AddSmoothedScore

Calculate smoothed expression values

Augments ggplot2 scatterplot with a PNG image.

Calculate module scores for gene expression programs in single cells

Assess Cluster Split

AverageExpression

Averaged gene expression by identity class

Assess Internal Nodes

AverageDetectionRate

Probability of detection by identity class

Average PCA scores by identity class

Run a custom distance function on an input data matrix

Match the case of character vectors

Align subspaces using dynamic time warping (DTW)

Create a custom color palette

CollapseSpeciesExpressionMatrix

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

CalcAlignmentMetric

Calculate an alignment score

CellCycleScoring

Score cell cycle phases

Cell-cell scatter plot

Color tSNE Plot Based on Split

CalcVarExpRatio

Calculate the ratio of variance explained by ICA or PCA to CCA

Classify New Data

Dimensional reduction heatmap

Sets identity class information to be a combination of two object attributes

A black and white color palette

CreateSeuratObject

Initialize and setup the Seurat object

Calculate the variance of logged values

Dimensional reduction plot

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Find cells with highest scores for a given dimensional reduction technique

Extract delimiter information from a string.

BuildRFClassifier

Build Random Forest Classifier

Diffusion Maps Cell Embeddings Accessor Function

Find genes with highest scores for a given dimensional reduction technique

SNN Graph Construction

Gene expression heatmap

Plot Diffusion map

DBClustDimension

Perform spectral density clustering on single cells

K-Means Clustering

Access cellular data

FastWhichCells Identify cells matching certain criteria (limited to character values)

Differential expression using DESeq2

Return a subset of the Seurat object

Vizualization of multiple features

Likelihood ratio test for zero-inflated data

Dot plot visualization

Gene expression markers for all identity classes

GetDimReduction

Dimensional Reduction Accessor Function

Differential expression testing using Student's t-test

Old Dot plot visualization (pre-ggplot implementation) Intuitive way of visualizing how gene expression changes across different identity classes (clusters). The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level of 'expressing' cells (green is high).

FindAllMarkersNode

Find all markers for a node

Gene expression markers of identity classes

Find cells with highest ICA scores

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Quickly Pick Relevant Dimensions

Find genes with highest ICA scores

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

Calculate the mean of logged values

FindVariableGenes

Identify variable genes

Accessor function for multimodal data

Get cell centroids

Build mixture models of gene expression

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Calculate the standard deviation of logged values

Demultiplex samples based on data from cell 'hashing'

Get Cluster Assignments

Infer spatial origins for single cells

Feature Locator

Determine statistical significance of PCA scores.

Make object sparse

Visualize 'features' on a dimensional reduction plot

Scatter plot of single cell data

ROC-based marker discovery

Independent component heatmap

Cluster Determination

Merge Seurat Objects

ICA Cell Embeddings Accessor Function

MatrixRowShuffle

Independently shuffle values within each row of a matrix

MetageneBicorPlot

Plot CC bicor saturation plot

FindConservedMarkers

Finds markers that are conserved between the two groups

ICA Gene Loadings Accessor Function

Merge childen of a node

Calculate the variance to mean ratio of logged values

NegBinomRegDETest

Negative binomial test for UMI-count based data (regularized version)

KClustDimension

Perform spectral k-means clustering on single cells

Differential expression using MAST

PCA Cell Embeddings Accessor Function

Apply a ceiling and floor to all values in a matrix

Negative binomial test for UMI-count based data

PCA Gene Loadings Accessor Function

Print PCA Calculation Parameters

Plot k-means clusters

Normalize Assay Data

Quickly Pick Relevant PCs

PlotClusterTree

Plot phylogenetic tree

Print SNN Construction Calculation Parameters

Normalize raw data

Poisson test for UMI-count based data

Principal component heatmap

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

Print ICA Calculation Parameters

Convert the cluster labels to a numeric representation

Print CCA Calculation Parameters

Print the results of a PCA analysis

PrintFindClustersParams

Print FindClusters Calculation Parameters

Gene expression heatmap

Load in data from 10X

Print the results of a ICA analysis

Print Diffusion Map Calculation Parameters

Find cells with highest PCA scores

Quantitative refinement of spatial inferences

Rename one identity class to another

Print the results of a dimensional reduction analysis

Run Principal Component Analysis on gene expression using IRLBA

Scale and center the data.

Find genes with highest PCA scores

Run t-distributed Stochastic Neighbor Embedding

RemoveFromTable

Remove data from a table

Old R based implementation of ScaleData. Scales and centers the data

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

PrintCalcParams

Print the calculation

Perform Canonical Correlation Analysis

Significant genes from a PCA

Project Principal Components Analysis onto full dataset

PrintTSNEParams

Print TSNE Calculation Parameters

Run diffusion map

Splits object into a list of subsetted objects.

PurpleAndYellow

A purple and yellow color palette

Project Dimensional reduction onto full dataset

SubsetByPredicate

Return a subset of the Seurat object.

Run Independent Component Analysis on gene expression

Set identity class information

Perform Canonical Correlation Analysis with more than two groups

Return a subset of columns for a matrix or data frame

Set identity class information

Set Cluster Assignments

Save cluster assignments to a TSV file

Single cell violin plot

ValidateClusters

Cluster Validation

SetDimReduction

Dimensional Reduction Mutator Function

Seurat-deprecated

Deprecated function(s) in the Seurat package

ValidateSpecificClusters

Specific Cluster Validation

Identify cells matching certain criteria

Visualize ICA genes

Transfer identity class information (or meta data) from one object to another

A small example version of the PBMC dataset

Shuffle a vector

Differential expression using Wilcoxon Rank Sum

The Seurat Class

UpdateSeuratObject

Update old Seurat object to accomodate new features

Split Dot plot visualization

Visualize PCA genes

Cell cycle genes

Reorder identity classes

Differential expression testing using Tobit models

Single cell ridge plot

VariableGenePlot

View variable genes

Switch identity class definition to another variable

VizDimReduction

Visualize Dimensional Reduction genes

Assay Data Mutator Function

show method for seurat

Return a subset of the Seurat object

Return a subset of rows for a matrix or data frame

Zebrafish analysis functions