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Seurat v2.2

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

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

2.2.1

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 (2.2.1)

AddSmoothedScore

Calculate smoothed expression values

BuildRFClassifier

Build Random Forest Classifier

Assess Internal Nodes

Assess Cluster Split

Align subspaces using dynamic time warping (DTW)

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

BuildClusterTree

Phylogenetic Analysis of Identity Classes

CellCycleScoring

Score cell cycle phases

Plot Diffusion map

Cell-cell scatter plot

FindConservedMarkers

Finds markers that are conserved between the two groups

Diffusion Maps Cell Embeddings Accessor Function

Run a custom distance function on an input data matrix

Create a custom color palette

Gene expression heatmap

Dot plot visualization

K-Means Clustering

Dimensional reduction plot

Dimensional reduction heatmap

Accessor function for multimodal data

Return a subset of the Seurat object

Vizualization of multiple features

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).

Cluster Determination

PrintCalcParams

Print the calculation

KClustDimension

Perform spectral k-means clustering on single cells

ICA Cell Embeddings Accessor Function

Calculate the variance to mean ratio of logged values

Run diffusion map

AddImputedScore

Calculate imputed expression values

DBClustDimension

Perform spectral density clustering on single cells

Calculate module scores for gene expression programs in single cells

Access cellular data

Plot k-means clusters

Assay Data Mutator Function

Normalize raw data

PCA Gene Loadings Accessor Function

SetDimReduction

Dimensional Reduction Mutator Function

Add samples into existing Seurat object.

Print CCA Calculation Parameters

CalcAlignmentMetric

Calculate an alignment score

Differential expression testing using Student's t-test

SNN Graph Construction

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Set identity class information

MetageneBicorPlot

Plot CC bicor saturation plot

Differential expression using DESeq2

Run Independent Component Analysis on gene expression

Find genes with highest ICA scores

Print SNN Construction Calculation Parameters

Find cells with highest ICA scores

Quickly Pick Relevant Dimensions

AverageDetectionRate

Probability of detection by identity class

Feature Locator

Infer spatial origins for single cells

Visualize 'features' on a dimensional reduction plot

Get cell centroids

FindAllMarkersNode

Find all markers for a node

Independent component heatmap

Single cell violin plot

Average PCA scores by identity class

Calculate the variance of logged values

A black and white color palette

Gene expression markers for all identity classes

Negative binomial test for UMI-count based data

CollapseSpeciesExpressionMatrix

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

Rename one identity class to another

CalcVarExpRatio

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

Classify New Data

NegBinomRegDETest

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

Apply a ceiling and floor to all values in a matrix

Find genes with highest PCA scores

PlotClusterTree

Plot phylogenetic tree

PrintTSNEParams

Print TSNE Calculation Parameters

Differential expression testing using Tobit models

Get Cluster Assignments

Differential expression using MAST

Likelihood ratio test for zero-inflated data

Match the case of character vectors

Set Cluster Assignments

Set identity class information

UpdateSeuratObject

Update old Seurat object to accomodate new features

Save cluster assignments to a TSV file

AverageExpression

Averaged gene expression by identity class

Return a subset of columns for a matrix or data frame

Project Dimensional reduction onto full dataset

VariableGenePlot

View variable genes

Print the results of a PCA analysis

Significant genes from a PCA

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Reorder identity classes

Project Principal Components Analysis onto full dataset

Make object sparse

Split Dot plot visualization

Extract delimiter information from a string.

Principal component heatmap

ICA Gene Loadings Accessor Function

Build mixture models of gene expression

FindVariableGenes

Identify variable genes

Quickly Pick Relevant PCs

Return a subset of the Seurat object

Scale and center the data.

Print Diffusion Map Calculation Parameters

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Single cell ridge plot

GetDimReduction

Dimensional Reduction Accessor Function

Perform Canonical Correlation Analysis

Print PCA Calculation Parameters

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

Identify cells matching certain criteria

show method for seurat

Switch identity class definition to another variable

Calculate the standard deviation of logged values

VizDimReduction

Visualize Dimensional Reduction genes

Zebrafish analysis functions

Color tSNE Plot Based on Split

CreateSeuratObject

Initialize and setup the Seurat object

Find genes with highest scores for a given dimensional reduction technique

Find cells with highest scores for a given dimensional reduction technique

Calculate the mean of logged values

Scatter plot of single cell data

Merge childen of a node

ValidateClusters

Cluster Validation

Shuffle a vector

Merge Seurat Objects

Determine statistical significance of PCA scores.

Normalize Assay Data

Print the results of a ICA analysis

Convert the cluster labels to a numeric representation

Print the results of a dimensional reduction analysis

Find cells with highest PCA scores

Quantitative refinement of spatial inferences

PrintFindClustersParams

Print FindClusters Calculation Parameters

Run t-distributed Stochastic Neighbor Embedding

RemoveFromTable

Remove data from a table

Seurat-deprecated

Deprecated function(s) in the Seurat package

Gene expression markers of identity classes

Gene expression heatmap

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

Perform Canonical Correlation Analysis with more than two groups

ROC-based marker discovery

Cell cycle genes

ValidateSpecificClusters

Specific Cluster Validation

MatrixRowShuffle

Independently shuffle values within each row of a matrix

The Seurat Class

A small example version of the PBMC dataset

PCA Cell Embeddings Accessor Function

Visualize PCA genes

Poisson test for UMI-count based data

Return a subset of rows for a matrix or data frame

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

PurpleAndYellow

A purple and yellow color palette

Print ICA Calculation Parameters

Load in data from 10X

Run Principal Component Analysis on gene expression using IRLBA

Visualize ICA genes

Differential expression using Wilcoxon Rank Sum