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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

2.3.2

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 (2.3.2)

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

CellCycleScoring

Score cell cycle phases

Calculate the mean of logged values

Access cellular data

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).

Find cells with highest scores for a given dimensional reduction technique

Dimensional reduction plot

Classify New Data

Cell-cell scatter plot

Visualize 'features' on a dimensional reduction plot

Find genes with highest scores for a given dimensional reduction technique

Dimensional reduction heatmap

Quickly Pick Relevant Dimensions

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

DBClustDimension

Perform spectral density clustering on single cells

Gene expression markers for all identity classes

Return a subset of the Seurat object

Gene expression heatmap

CreateSeuratObject

Initialize and setup the Seurat object

Create a custom color palette

Get cell centroids

Likelihood ratio test for zero-inflated data

MetageneBicorPlot

Plot CC bicor saturation plot

Color tSNE Plot Based on Split

Convert Seurat objects to other classes and vice versa

Run a custom distance function on an input data matrix

Find genes with highest ICA scores

Determine statistical significance of PCA scores.

Differential expression using DESeq2

Infer spatial origins for single cells

CollapseSpeciesExpressionMatrix

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

Extract delimiter information from a string.

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

Cluster Determination

Diffusion Maps Cell Embeddings Accessor Function

FindAllMarkersNode

Find all markers for a node

Differential expression testing using Student's t-test

Plot Diffusion map

Apply a ceiling and floor to all values in a matrix

Accessor function for multimodal data

ICA Cell Embeddings Accessor Function

K-Means Clustering

Dot plot visualization

Scale and center the data.

Project Dimensional reduction onto full dataset

Run diffusion map

PCA Gene Loadings Accessor Function

Save cluster assignments to a TSV file

Run Independent Component Analysis on gene expression

Print the results of a ICA analysis

Print ICA Calculation Parameters

Vizualization of multiple features

Normalize raw data

Project Principal Components Analysis onto full dataset

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

Calculate the variance to mean ratio of logged values

Calculate the standard deviation of logged values

Feature Locator

Merge childen of a node

FindVariableGenes

Identify variable genes

Set identity class information

Merge Seurat Objects

SubsetByPredicate

Return a subset of the Seurat object.

Independent component heatmap

Find cells with highest ICA scores

Find genes with highest PCA scores

Get Cluster Assignments

GetDimReduction

Dimensional Reduction Accessor Function

Differential expression using MAST

Calculate the variance of logged values

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Make object sparse

Negative binomial test for UMI-count based data

NegBinomRegDETest

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

Demultiplex samples based on data from cell 'hashing'

ROC-based marker discovery

FindConservedMarkers

Finds markers that are conserved between the two groups

PlotClusterTree

Plot phylogenetic tree

Poisson test for UMI-count based data

MatrixRowShuffle

Independently shuffle values within each row of a matrix

Gene expression markers of identity classes

Build mixture models of gene expression

Scatter plot of single cell data

ICA Gene Loadings Accessor Function

VizDimReduction

Visualize Dimensional Reduction genes

KClustDimension

Perform spectral k-means clustering on single cells

Normalize Assay Data

Plot k-means clusters

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

Gene expression heatmap

Convert the cluster labels to a numeric representation

PrintFindClustersParams

Print FindClusters Calculation Parameters

Print the results of a dimensional reduction analysis

Print the results of a PCA analysis

RemoveFromTable

Remove data from a table

PCA Cell Embeddings Accessor Function

Print PCA Calculation Parameters

Read 10X hdf5 file

Principal component heatmap

Visualize ICA genes

Quantitative refinement of spatial inferences

Cell cycle genes

A small example version of the PBMC dataset

Find cells with highest PCA scores

Print CCA Calculation Parameters

Rename one identity class to another

Reorder identity classes

PrintCalcParams

Print the calculation

Significant genes from a PCA

Perform Canonical Correlation Analysis with more than two groups

PurpleAndYellow

A purple and yellow color palette

Quickly Pick Relevant PCs

SetDimReduction

Dimensional Reduction Mutator Function

Run Principal Component Analysis on gene expression using IRLBA

Print Diffusion Map Calculation Parameters

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Set identity class information

Split Dot plot visualization

Return a subset of columns for a matrix or data frame

Return a subset of the Seurat object

Load in data from 10X

Splits object into a list of subsetted objects.

VariableGenePlot

View variable genes

Single cell ridge plot

UpdateSeuratObject

Update old Seurat object to accomodate new features

Perform Canonical Correlation Analysis

Print SNN Construction Calculation Parameters

ValidateSpecificClusters

Specific Cluster Validation

PrintTSNEParams

Print TSNE Calculation Parameters

ValidateClusters

Cluster Validation

Identify cells matching certain criteria

Run t-distributed Stochastic Neighbor Embedding

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

Switch identity class definition to another variable

Assay Data Mutator Function

Seurat-deprecated

Deprecated function(s) in the Seurat package

Shuffle a vector

Set Cluster Assignments

Differential expression testing using Tobit models

Differential expression using Wilcoxon Rank Sum

Return a subset of rows for a matrix or data frame

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

The Seurat Class

Single cell violin plot

Visualize PCA genes

Add samples into existing Seurat object.

AverageDetectionRate

Probability of detection by identity class

Assess Internal Nodes

AddSmoothedScore

Calculate smoothed expression values

Calculate module scores for gene expression programs in single cells

Augments ggplot2 scatterplot with a PNG image.

AddImputedScore

Calculate imputed expression values

Align subspaces using dynamic time warping (DTW)

Assess Cluster Split

AverageExpression

Averaged gene expression by identity class

Match the case of character vectors

A black and white color palette

CalcVarExpRatio

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

CalcAlignmentMetric

Calculate an alignment score

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Average PCA scores by identity class

BuildRFClassifier

Build Random Forest Classifier

SNN Graph Construction

Zebrafish analysis functions