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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

2.3.4

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

July 20th, 2018

Functions in Seurat (2.3.4)

Classify New Data

Match the case of character vectors

CollapseSpeciesExpressionMatrix

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

Gene expression markers for all identity classes

FindAllMarkersNode

Find all markers for a node

Differential expression testing using Student's t-test

Find gene terms from Enrichr

Access cellular data

Find cells with highest scores for a given dimensional reduction technique

Run a custom distance function on an input data matrix

Find genes 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).

Dot plot visualization

Quickly Pick Relevant Dimensions

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Gene expression markers of identity classes

Return a subset of the Seurat object

Color tSNE Plot Based on Split

Create a custom color palette

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

Convert Seurat objects to other classes and vice versa

Get cell centroids

Dimensional reduction heatmap

Get Cluster Assignments

GetDimReduction

Dimensional Reduction Accessor Function

Dimensional reduction plot

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

Gene expression heatmap

K-Means Clustering

Vizualization of multiple features

KClustDimension

Perform spectral k-means clustering on single cells

Plot k-means clusters

CreateSeuratObject

Initialize and setup the Seurat object

Calculate the mean of logged values

Likelihood ratio test for zero-inflated data

Calculate the standard deviation of logged values

Diffusion Maps Cell Embeddings Accessor Function

ICA Gene Loadings Accessor Function

CellCycleScoring

Score cell cycle phases

ROC-based marker discovery

Plot Diffusion map

Feature Locator

Normalize raw data

Visualize 'features' on a dimensional reduction plot

Quickly Pick Relevant PCs

MatrixRowShuffle

Independently shuffle values within each row of a matrix

Significant genes from a PCA

Calculate the variance to mean ratio of logged values

Gene expression heatmap

Cell-cell scatter plot

Calculate the variance of logged values

DBClustDimension

Perform spectral density clustering on single cells

Differential expression using DESeq2

Extract delimiter information from a string.

PCA Cell Embeddings Accessor Function

Principal component heatmap

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

Cluster Determination

FindConservedMarkers

Finds markers that are conserved between the two groups

Print Diffusion Map Calculation Parameters

Print SNN Construction Calculation Parameters

Find cells with highest PCA scores

Build mixture models of gene expression

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

FindVariableGenes

Identify variable genes

PrintTSNEParams

Print TSNE Calculation Parameters

Get identity of cells

Single cell ridge plot

Run Canonical Correlation Analysis (CCA) on multimodal data

Scatter plot of single cell data

ICA Cell Embeddings Accessor Function

Find genes with highest ICA scores

Print the results of a ICA analysis

Perform Canonical Correlation Analysis

Print ICA Calculation Parameters

Run coinertia analysis on multimodal data

Read 10X hdf5 file

Accessor function for multimodal data

Determine statistical significance of PCA scores.

Poisson test for UMI-count based data

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

Switch identity class definition to another variable

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

Demultiplex samples based on data from cell 'hashing'

Infer spatial origins for single cells

Merge childen of a node

Quantitative refinement of spatial inferences

Perform Canonical Correlation Analysis with more than two groups

Run Principal Component Analysis on gene expression using IRLBA

Return a subset of columns for a matrix or data frame

Project Dimensional reduction onto full dataset

MetageneBicorPlot

Plot CC bicor saturation plot

Project Principal Components Analysis onto full dataset

Assay Data Mutator Function

Return a subset of the Seurat object

PurpleAndYellow

A purple and yellow color palette

Merge Seurat Objects

Negative binomial test for UMI-count based data

Hashtag oligo heatmap

Set Cluster Assignments

Single cell violin plot

Visualize PCA genes

Independent component heatmap

Find cells with highest ICA scores

Differential expression using MAST

Set identity class information

Make object sparse

Normalize Assay Data

SubsetByPredicate

Return a subset of the Seurat object.

Apply a ceiling and floor to all values in a matrix

Load in data from 10X

PCA Gene Loadings Accessor Function

Print CCA Calculation Parameters

NegBinomRegDETest

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

Print the results of a dimensional reduction analysis

A small example version of the PBMC dataset

Cell cycle genes

PrintFindClustersParams

Print FindClusters Calculation Parameters

Convert the cluster labels to a numeric representation

Run diffusion map

Print the results of a PCA analysis

PrintCalcParams

Print the calculation

Print PCA Calculation Parameters

Find genes with highest PCA scores

RemoveFromTable

Remove data from a table

Seurat-deprecated

Deprecated function(s) in the Seurat package

Shuffle a vector

PlotClusterTree

Plot phylogenetic tree

VizDimReduction

Visualize Dimensional Reduction genes

Run Independent Component Analysis on gene expression

Rename one identity class to another

Split Dot plot visualization

Reorder identity classes

Splits object into a list of subsetted objects.

Run t-distributed Stochastic Neighbor Embedding

Save cluster assignments to a TSV file

Return a subset of rows for a matrix or data frame

Visualize ICA genes

Scale and center the data.

SetDimReduction

Dimensional Reduction Mutator Function

UpdateSeuratObject

Update old Seurat object to accomodate new features

Set identity class information

Differential expression testing using Tobit models

Identify cells matching certain criteria

ValidateClusters

Cluster Validation

Differential expression using Wilcoxon Rank Sum

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

ValidateSpecificClusters

Specific Cluster Validation

The Seurat Class

VariableGenePlot

View variable genes

Identify potential genes associated with batch effects

A black and white color palette

Assess Internal Nodes

Assess Cluster Split

SNN Graph Construction

Calculate module scores for gene expression programs in single cells

Add samples into existing Seurat object.

CalcAlignmentMetric

Calculate an alignment score

CalcVarExpRatio

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

AddSmoothedScore

Calculate smoothed expression values

Align subspaces using dynamic time warping (DTW)

BuildClusterTree

Phylogenetic Analysis of Identity Classes

BuildRFClassifier

Build Random Forest Classifier

Augments ggplot2 scatterplot with a PNG image.

AverageDetectionRate

Probability of detection by identity class

AverageExpression

Averaged gene expression by identity class

Average PCA scores by identity class

AddImputedScore

Calculate imputed expression values