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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

58,682

Version

2.3.1

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 (2.3.1)

Classify New Data

Likelihood ratio test for zero-inflated data

Color tSNE Plot Based on Split

Calculate the standard deviation of logged values

Normalize raw data

CollapseSpeciesExpressionMatrix

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

Calculate the variance of logged values

Run a custom distance function on an input data matrix

Gene expression markers of identity classes

Dimensional reduction heatmap

Determine statistical significance of PCA scores.

FindConservedMarkers

Finds markers that are conserved between the two groups

Dimensional reduction plot

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

CreateSeuratObject

Initialize and setup the Seurat object

Find cells with highest scores for a given dimensional reduction technique

Quickly Pick Relevant Dimensions

Calculate the mean of logged values

ICA Gene Loadings Accessor Function

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).

Return a subset of the Seurat object

FindAllMarkersNode

Find all markers for a node

A black and white color palette

Get cell centroids

Vizualization of multiple features

Find cells with highest ICA scores

K-Means Clustering

Independent component heatmap

Differential expression testing using Student's t-test

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Convert Seurat objects to other classes and vice versa

Run Principal Component Analysis on gene expression using IRLBA

Find genes with highest scores for a given dimensional reduction technique

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Gene expression heatmap

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

Create a custom color palette

Poisson test for UMI-count based data

Calculate the variance to mean ratio of logged values

DBClustDimension

Perform spectral density clustering on single cells

Accessor function for multimodal data

Build mixture models of gene expression

Cluster Determination

Differential expression using DESeq2

ROC-based marker discovery

Splits object into a list of subsetted objects.

Gene expression markers for all identity classes

Plot Diffusion map

Make object sparse

Project Dimensional reduction onto full dataset

Gene expression heatmap

Perform Canonical Correlation Analysis with more than two groups

Demultiplex samples based on data from cell 'hashing'

PCA Gene Loadings Accessor Function

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

Differential expression using MAST

FindVariableGenes

Identify variable genes

PCA Cell Embeddings Accessor Function

Access cellular data

Extract delimiter information from a string.

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Apply a ceiling and floor to all values in a matrix

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

Significant genes from a PCA

Differential expression testing using Tobit models

GetDimReduction

Dimensional Reduction Accessor Function

Principal component heatmap

Diffusion Maps Cell Embeddings Accessor Function

Set Cluster Assignments

Merge Seurat Objects

Print Diffusion Map Calculation Parameters

Infer spatial origins for single cells

Get Cluster Assignments

Visualize 'features' on a dimensional reduction plot

MatrixRowShuffle

Independently shuffle values within each row of a matrix

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

Merge childen of a node

MetageneBicorPlot

Plot CC bicor saturation plot

Run t-distributed Stochastic Neighbor Embedding

Dot plot visualization

Normalize Assay Data

Reorder identity classes

ICA Cell Embeddings Accessor Function

Set identity class information

Convert the cluster labels to a numeric representation

Print the results of a dimensional reduction analysis

Find cells with highest PCA scores

PrintFindClustersParams

Print FindClusters Calculation Parameters

Switch identity class definition to another variable

Scale and center the data.

Quickly Pick Relevant PCs

Quantitative refinement of spatial inferences

ValidateSpecificClusters

Specific Cluster Validation

Feature Locator

Find genes with highest PCA scores

Return a subset of the Seurat object

SetDimReduction

Dimensional Reduction Mutator Function

Rename one identity class to another

Print the results of a ICA analysis

Print ICA Calculation Parameters

Save cluster assignments to a TSV file

Run Independent Component Analysis on gene expression

Find genes with highest ICA scores

Run diffusion map

VizDimReduction

Visualize Dimensional Reduction genes

Seurat-deprecated

Deprecated function(s) in the Seurat package

Scatter plot of single cell data

Project Principal Components Analysis onto full dataset

VariableGenePlot

View variable genes

Print SNN Construction Calculation Parameters

ValidateClusters

Cluster Validation

RemoveFromTable

Remove data from a table

Load in data from 10X

Print CCA Calculation Parameters

The Seurat Class

Set identity class information

Single cell violin plot

Split Dot plot visualization

KClustDimension

Perform spectral k-means clustering on single cells

A small example version of the PBMC dataset

PrintCalcParams

Print the calculation

PrintTSNEParams

Print TSNE Calculation Parameters

Shuffle a vector

Return a subset of rows for a matrix or data frame

Identify cells matching certain criteria

show method for seurat

Assay Data Mutator Function

UpdateSeuratObject

Update old Seurat object to accomodate new features

Differential expression using Wilcoxon Rank Sum

Plot k-means clusters

Negative binomial test for UMI-count based data

Cell cycle genes

Print the results of a PCA analysis

NegBinomRegDETest

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

PlotClusterTree

Plot phylogenetic tree

Print PCA Calculation Parameters

PurpleAndYellow

A purple and yellow color palette

Single cell ridge plot

SubsetByPredicate

Return a subset of the Seurat object.

Return a subset of columns for a matrix or data frame

Perform Canonical Correlation Analysis

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

Visualize PCA genes

Visualize ICA genes

AddSmoothedScore

Calculate smoothed expression values

Assess Internal Nodes

AverageDetectionRate

Probability of detection by identity class

AddImputedScore

Calculate imputed expression values

Augments ggplot2 scatterplot with a PNG image.

Add samples into existing Seurat object.

Calculate module scores for gene expression programs in single cells

Average PCA scores by identity class

Align subspaces using dynamic time warping (DTW)

Assess Cluster Split

AverageExpression

Averaged gene expression by identity class

CalcAlignmentMetric

Calculate an alignment score

Match the case of character vectors

BuildRFClassifier

Build Random Forest Classifier

SNN Graph Construction

CalcVarExpRatio

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

Cell-cell scatter plot

CellCycleScoring

Score cell cycle phases

Zebrafish analysis functions