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Seurat v2.0

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

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

2.0.0

License

GPL-3

Maintainer

Last Published

May 10th, 2024

Functions in Seurat (2.0.0)

Assess Internal Nodes

Assess Cluster Split

AverageDetectionRate

Probability of detection by identity class

AverageExpression

Averaged gene expression by identity class

AddImputedScore

Calculate imputed expression values

AddSmoothedScore

Calculate smoothed expression values

Align subspaces using dynamic time warping (DTW)

Calculate module scores for gene expression programs in single cells

Add samples into existing Seurat object.

Average PCA scores by identity class

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Likelihood ratio test for zero-inflated data

Differential expression testing using Student's t-test

Classify New Data

Color tSNE Plot Based on Split

CreateSeuratObject

Initialize and setup the Seurat object

Run a custom distance function on an input data matrix

CalcVarExpRatio

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

Match the case of character vectors

Create a custom color palette

Find genes with highest scores for a given dimensional reduction technique

Gene expression heatmap

Visualize 'features' on a dimensional reduction plot

Access cellular data

BuildRFClassifier

Build Random Forest Classifier

SNN Graph Construction

Diffusion Maps Cell Embeddings Accessor Function

Diffusion Maps Gene Loading Accessor Function

Accessor function for multimodal data

Independent component heatmap

CellCycleScoring

Score cell cycle phases

Cell-cell scatter plot

Plot Diffusion map

Quickly Pick Relevant Dimensions

Dimensional reduction heatmap

Return a subset of the Seurat object

Gene expression markers for all identity classes

Dimensional reduction plot

Find cells with highest scores for a given dimensional reduction technique

Calculate the standard deviation of logged values

Calculate the variance of logged values

FindAllMarkersNode

Find all markers for a node

Cluster Determination

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Get cell centroids

Build mixture models of gene expression

Scatter plot of single cell data

Get Cluster Assignments

GetDimReduction

Dimensional Reduction Accessor Function

Single cell joy plot

Make object sparse

ROC-based marker discovery

Infer spatial origins for single cells

Determine statistical significance of PCA scores.

Negative binomial test for UMI-count based data

NegBinomRegDETest

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

Quickly Pick Relevant PCs

Principal component heatmap

PlotClusterTree

Plot phylogenetic tree

Normalize raw data

Calculate the variance to mean ratio of logged values

Merge Seurat Objects

Apply a ceiling and floor to all values in a matrix

DBClustDimension

Perform spectral density clustering on single cells

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

Extract delimiter information from a string.

PCA Cell Embeddings Accessor Function

PCA Gene Loadings Accessor Function

PrintCalcParams

Print the calculation

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Convert the cluster labels to a numeric representation

Gene expression heatmap

Print Diffusion Map Calculation Parameters

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

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

FindVariableGenes

Identify variable genes

ICA Cell Embeddings Accessor Function

Poisson test for UMI-count based data

Print ICA Calculation Parameters

Print the results of a PCA analysis

Run diffusion map

Print the results of a dimensional reduction analysis

Project Principal Components Analysis onto full dataset

Load in data from 10X

RemoveFromTable

Remove data from a table

Rename one identity class to another

Print PCA Calculation Parameters

Print SNN Construction Calculation Parameters

Reorder identity classes

Perform Canonical Correlation Analysis

Scale and center the data.

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

UpdateSeuratObject

Update old Seurat object to accomodate new features

ValidateClusters

Cluster Validation

Single cell violin plot

Run Independent Component Analysis on gene expression

Set identity class information

Seurat-deprecated

Deprecated function(s) in the Seurat package

Set identity class information

K-Means Clustering

Dot plot visualization

Vizualization of multiple features

Feature Locator

Return a subset of columns for a matrix or data frame

Visualize ICA genes

Visualize PCA genes

Set Cluster Assignments

SetDimReduction

Dimensional Reduction Mutator Function

ValidateSpecificClusters

Specific Cluster Validation

VariableGenePlot

View variable genes

The Seurat Class

Draw 3D in situ predictions from Zebrafish dataset

ICA Gene Loadings Accessor Function

Find cells with highest ICA scores

Find genes with highest ICA scores

FindConservedMarkers

Finds markers that are conserved between the two groups

Gene expression markers of identity classes

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Identify cells matching certain criteria

Normalize Assay Data

Find cells with highest PCA scores

Find genes with highest PCA scores

PrintFindClustersParams

Print FindClusters Calculation Parameters

Print the results of a PCA analysis

Quantitative refinement of spatial inferences

RegressOutResid

Regress out technical effects and cell cycle

Run Principal Component Analysis on gene expression using IRLBA

Run t-distributed Stochastic Neighbor Embedding

Switch identity class definition to another variable

Assay Data Mutator Function

Differential expression testing using Tobit models

VizClassification

Highlight classification results

VizDimReduction

Visualize Dimensional Reduction genes

Plot k-means clusters

MatrixRowShuffle

Independently shuffle values within each row of a matrix

Merge childen of a node

KClustDimension

Perform spectral k-means clustering on single cells

Significant genes from a PCA

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

Print CCA Calculation Parameters

PrintTSNEParams

Print TSNE Calculation Parameters

Project Dimensional reduction onto full dataset

Save cluster assignments to a TSV file

Shuffle a vector

Split Dot plot visualization

Return a subset of the Seurat object

Return a subset of rows for a matrix or data frame