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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

49,115

Version

2.3.3

License

GPL-3 | file LICENSE

Issues

Pull Requests

Stars

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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 (2.3.3)

CalcVarExpRatio

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

DBClustDimension

Perform spectral density clustering on single cells

Create a custom color palette

Color tSNE Plot Based on Split

Cell-cell scatter plot

Convert Seurat objects to other classes and vice versa

Quickly Pick Relevant Dimensions

CreateSeuratObject

Initialize and setup the Seurat object

CollapseSpeciesExpressionMatrix

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

Classify New Data

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

Dimensional reduction heatmap

Plot Diffusion map

Visualize 'features' on a dimensional reduction plot

Dimensional reduction plot

Find cells with highest scores for a given dimensional reduction technique

Diffusion Maps Cell Embeddings Accessor Function

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

Scatter plot of single cell data

Access cellular data

Calculate the variance of logged values

Extract delimiter information from a string.

Calculate the standard deviation of logged values

Build mixture models of gene expression

K-Means Clustering

Run a custom distance function on an input data matrix

Differential expression using DESeq2

Dot plot visualization

Likelihood ratio test for zero-inflated data

FindConservedMarkers

Finds markers that are conserved between the two groups

Gene expression markers of identity classes

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Differential expression testing using Student's t-test

Return a subset of the Seurat object

Get cell centroids

Gene expression markers for all identity classes

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

Calculate the variance to mean ratio of logged values

ICA Gene Loadings Accessor Function

Independent component heatmap

ICA Cell Embeddings Accessor Function

Get Cluster Assignments

Differential expression using MAST

Apply a ceiling and floor to all values in a matrix

FindVariableGenes

Identify variable genes

GetDimReduction

Dimensional Reduction Accessor Function

Negative binomial test for UMI-count based data

Find cells with highest ICA scores

Find genes with highest ICA scores

Plot k-means clusters

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).

Calculate the mean of logged values

Make object sparse

Find cells with highest PCA scores

Normalize raw data

FindAllMarkersNode

Find all markers for a node

Find genes with highest scores for a given dimensional reduction technique

Gene expression heatmap

NegBinomRegDETest

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

Convert the cluster labels to a numeric representation

ROC-based marker discovery

MatrixRowShuffle

Independently shuffle values within each row of a matrix

Find genes with highest PCA scores

Quickly Pick Relevant PCs

Gene expression heatmap

Cluster Determination

Merge childen of a node

PCA Gene Loadings Accessor Function

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Accessor function for multimodal data

PCA Cell Embeddings Accessor Function

Demultiplex samples based on data from cell 'hashing'

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

Infer spatial origins for single cells

PrintFindClustersParams

Print FindClusters Calculation Parameters

Principal component heatmap

Print the results of a ICA analysis

Vizualization of multiple features

Print ICA Calculation Parameters

Determine statistical significance of PCA scores.

Project Principal Components Analysis onto full dataset

PlotClusterTree

Plot phylogenetic tree

Poisson test for UMI-count based data

Print CCA Calculation Parameters

Print the results of a PCA analysis

Quantitative refinement of spatial inferences

RemoveFromTable

Remove data from a table

PurpleAndYellow

A purple and yellow color palette

Feature Locator

Reorder identity classes

PrintTSNEParams

Print TSNE Calculation Parameters

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

Print PCA Calculation Parameters

Hashtag oligo heatmap

Perform Canonical Correlation Analysis

Run diffusion map

Normalize Assay Data

KClustDimension

Perform spectral k-means clustering on single cells

Print SNN Construction Calculation Parameters

Project Dimensional reduction onto full dataset

Merge Seurat Objects

Rename one identity class to another

Run t-distributed Stochastic Neighbor Embedding

UpdateSeuratObject

Update old Seurat object to accomodate new features

PrintCalcParams

Print the calculation

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Load in data from 10X

MetageneBicorPlot

Plot CC bicor saturation plot

Single cell ridge plot

Read 10X hdf5 file

Run Independent Component Analysis on gene expression

Run Principal Component Analysis on gene expression using IRLBA

Set identity class information

Scale and center the data.

Perform Canonical Correlation Analysis with more than two groups

Splits object into a list of subsetted objects.

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

Significant genes from a PCA

Print Diffusion Map Calculation Parameters

Set identity class information

Seurat-deprecated

Deprecated function(s) in the Seurat package

Switch identity class definition to another variable

Save cluster assignments to a TSV file

Print the results of a dimensional reduction analysis

Differential expression testing using Tobit models

SubsetByPredicate

Return a subset of the Seurat object.

Visualize ICA genes

Single cell violin plot

Set Cluster Assignments

Return a subset of columns for a matrix or data frame

Assay Data Mutator Function

Visualize PCA genes

A small example version of the PBMC dataset

Identify cells matching certain criteria

Split Dot plot visualization

Shuffle a vector

SetDimReduction

Dimensional Reduction Mutator Function

The Seurat Class

ValidateClusters

Cluster Validation

Return a subset of rows for a matrix or data frame

Return a subset of the Seurat object

VariableGenePlot

View variable genes

VizDimReduction

Visualize Dimensional Reduction genes

ValidateSpecificClusters

Specific Cluster Validation

Differential expression using Wilcoxon Rank Sum

Cell cycle genes

AverageExpression

Averaged gene expression by identity class

Augments ggplot2 scatterplot with a PNG image.

AverageDetectionRate

Probability of detection by identity class

CellCycleScoring

Score cell cycle phases

Add samples into existing Seurat object.

Match the case of character vectors

Calculate module scores for gene expression programs in single cells

AddSmoothedScore

Calculate smoothed expression values

CalcAlignmentMetric

Calculate an alignment score

Assess Internal Nodes

AddImputedScore

Calculate imputed expression values

BuildRFClassifier

Build Random Forest Classifier

Assess Cluster Split

SNN Graph Construction

Align subspaces using dynamic time warping (DTW)

Average PCA scores by identity class

A black and white color palette

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Zebrafish analysis functions