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Seurat v2.1

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 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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Install

install.packages('Seurat')

Monthly Downloads

49,115

Version

2.1.0

License

GPL-3 | file LICENSE

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Repository

https://github.com/satijalab/seurat

Homepage

http://www.satijalab.org/seurat

Maintainer

Last Published

May 10th, 2024

Functions in Seurat (2.1.0)

AddSmoothedScore

Calculate smoothed expression values

Align subspaces using dynamic time warping (DTW)

AverageDetectionRate

Probability of detection by identity class

AverageExpression

Averaged gene expression by identity class

Assess Internal Nodes

Assess Cluster Split

Calculate module scores for gene expression programs in single cells

Add samples into existing Seurat object.

AddImputedScore

Calculate imputed expression values

Match the case of character vectors

CellCycleScoring

Score cell cycle phases

Plot Diffusion map

Cell-cell scatter plot

Classify New Data

Differential expression using DESeq2

Diffusion Maps Cell Embeddings Accessor Function

Average PCA scores by identity class

A black and white color palette

CollapseSpeciesExpressionMatrix

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

BuildClusterTree

Phylogenetic Analysis of Identity Classes

BuildRFClassifier

Build Random Forest Classifier

Create a custom color palette

DBClustDimension

Perform spectral density clustering on single cells

Likelihood ratio test for zero-inflated data

Differential expression testing using Student's t-test

Calculate the standard deviation of logged values

Calculate the variance of logged values

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

FindVariableGenes

Identify variable genes

Accessor function for multimodal data

Single cell joy plot

SNN Graph Construction

CalcVarExpRatio

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

CreateSeuratObject

Initialize and setup the Seurat object

Differential expression using MAST

Make object sparse

PCA Cell Embeddings Accessor Function

K-Means Clustering

Dot plot visualization

Visualize 'features' on a dimensional reduction plot

Access cellular data

Build mixture models of gene expression

Quickly Pick Relevant Dimensions

Dimensional reduction heatmap

Extract delimiter information from a string.

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

FindConservedMarkers

Finds markers that are conserved between the two groups

Gene expression markers of identity classes

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Get cell centroids

KClustDimension

Perform spectral k-means clustering on single cells

Plot k-means clusters

Merge childen of a node

Merge Seurat Objects

NegBinomRegDETest

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

Normalize Assay Data

PlotClusterTree

Plot phylogenetic tree

Poisson test for UMI-count based data

PrintFindClustersParams

Print FindClusters Calculation Parameters

Find genes with highest scores for a given dimensional reduction technique

Gene expression heatmap

Vizualization of multiple features

PCA Gene Loadings Accessor Function

PrintCalcParams

Print the calculation

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Print PCA Calculation Parameters

Color tSNE Plot Based on Split

Print SNN Construction Calculation Parameters

Run Principal Component Analysis on gene expression using IRLBA

Run t-distributed Stochastic Neighbor Embedding

Run a custom distance function on an input data matrix

Dimensional reduction plot

Find cells with highest scores for a given dimensional reduction technique

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).

Print the results of a ICA analysis

Reorder identity classes

Perform Canonical Correlation Analysis

Switch identity class definition to another variable

Assay Data Mutator Function

Calculate the mean of logged values

FindAllMarkersNode

Find all markers for a node

Cluster Determination

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Save cluster assignments to a TSV file

Set identity class information

Return a subset of columns for a matrix or data frame

Scatter plot of single cell data

Get Cluster Assignments

GetDimReduction

Dimensional Reduction Accessor Function

Find cells with highest ICA scores

Find genes with highest ICA scores

VizDimReduction

Visualize Dimensional Reduction genes

Visualize ICA genes

Feature Locator

Return a subset of the Seurat object

Gene expression markers for all identity classes

ICA Cell Embeddings Accessor Function

ICA Gene Loadings Accessor Function

Independent component heatmap

Apply a ceiling and floor to all values in a matrix

Negative binomial test for UMI-count based data

Infer spatial origins for single cells

Determine statistical significance of PCA scores.

Normalize raw data

Calculate the variance to mean ratio of logged values

ROC-based marker discovery

MatrixRowShuffle

Independently shuffle values within each row of a matrix

Quickly Pick Relevant PCs

Significant genes from a PCA

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

Print CCA Calculation Parameters

Principal component heatmap

Find cells with highest PCA scores

Find genes with highest PCA scores

Project Principal Components Analysis onto full dataset

Convert the cluster labels to a numeric representation

Gene expression heatmap

Print Diffusion Map Calculation Parameters

Print the results of a dimensional reduction analysis

Print ICA Calculation Parameters

Print the results of a PCA analysis

Run diffusion map

PrintTSNEParams

Print TSNE Calculation Parameters

Project Dimensional reduction onto full dataset

Load in data from 10X

Run Independent Component Analysis on gene expression

Set Cluster Assignments

SetDimReduction

Dimensional Reduction Mutator Function

UpdateSeuratObject

Update old Seurat object to accomodate new features

ValidateClusters

Cluster Validation

ValidateSpecificClusters

Specific Cluster Validation

VariableGenePlot

View variable genes

PurpleAndYellow

A purple and yellow color palette

RemoveFromTable

Remove data from a table

Rename one identity class to another

Set identity class information

Seurat-deprecated

Deprecated function(s) in the Seurat package

Differential expression testing using Tobit models

Quantitative refinement of spatial inferences

Scale and center the data.

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

Return a subset of the Seurat object

Return a subset of rows for a matrix or data frame

Visualize PCA genes

Single cell violin plot

Identify cells matching certain criteria

Differential expression using Wilcoxon Rank Sum

The Seurat Class

show method for seurat

Shuffle a vector

Split Dot plot visualization

Cell cycle genes

A small example version of the PBMC dataset

Zebrafish analysis functions