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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

44,926

Version

2.2.0

License

GPL-3 | file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/satijalab/seurat

Homepage

http://www.satijalab.org/seurat

Maintainer

Last Published

January 10th, 2018

Functions in Seurat (2.2.0)

AddSmoothedScore

Calculate smoothed expression values

Align subspaces using dynamic time warping (DTW)

AddImputedScore

Calculate imputed expression values

Calculate module scores for gene expression programs in single cells

Add samples into existing Seurat object.

AverageDetectionRate

Probability of detection by identity class

AverageExpression

Averaged gene expression by identity class

Assess Internal Nodes

Assess Cluster Split

SNN Graph Construction

CalcAlignmentMetric

Calculate an alignment score

Average PCA scores by identity class

A black and white color palette

Color tSNE Plot Based on Split

CreateSeuratObject

Initialize and setup the Seurat object

CalcVarExpRatio

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

Match the case of character vectors

Run a custom distance function on an input data matrix

BuildRFClassifier

Build Random Forest Classifier

DBClustDimension

Perform spectral density clustering on single cells

Gene expression heatmap

K-Means Clustering

Calculate the variance of logged values

Calculate the mean of logged values

Calculate the standard deviation of logged values

Cluster Determination

BuildClusterTree

Phylogenetic Analysis of Identity Classes

Extract delimiter information from a string.

Access cellular data

Return a subset of the Seurat object

Differential expression using DESeq2

Dimensional reduction heatmap

Dimensional reduction plot

CellCycleScoring

Score cell cycle phases

Cell-cell scatter plot

Diffusion Maps Cell Embeddings Accessor Function

Plot Diffusion map

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

Vizualization of multiple features

Gene expression markers for all identity classes

Create a custom color palette

Find cells with highest scores for a given dimensional reduction technique

Find genes with highest scores for a given dimensional reduction technique

Accessor function for multimodal data

GetCellEmbeddings

Dimensional Reduction Cell Embeddings Accessor Function

Find genes with highest ICA scores

Infer spatial origins for single cells

ROC-based marker discovery

MatrixRowShuffle

Independently shuffle values within each row of a matrix

Normalize Assay Data

Convert the cluster labels to a numeric representation

Poisson test for UMI-count based data

PrintAlignSubspaceParams

Print AlignSubspace Calculation Parameters

FindConservedMarkers

Finds markers that are conserved between the two groups

GetDimReduction

Dimensional Reduction Accessor Function

GetGeneLoadings

Dimensional Reduction Gene Loadings Accessor Function

Independent component heatmap

Find cells with highest ICA scores

Negative binomial test for UMI-count based data

Get cell centroids

Get Cluster Assignments

KClustDimension

Perform spectral k-means clustering on single cells

Plot k-means clusters

Normalize raw data

Calculate the variance to mean ratio of logged values

Likelihood ratio test for zero-inflated data

Feature Locator

Visualize 'features' on a dimensional reduction plot

FindVariableGenes

Identify variable genes

Build mixture models of gene expression

Scatter plot of single cell data

Classify New Data

CollapseSpeciesExpressionMatrix

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

Differential expression testing using Student's t-test

Quickly Pick Relevant Dimensions

Find genes with highest PCA scores

PlotClusterTree

Plot phylogenetic tree

Rename one identity class to another

Reorder identity classes

Visualize ICA genes

Dot plot visualization

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).

Gene expression markers of identity classes

FindMarkersNode

Gene expression markers of identity classes defined by a phylogenetic clade

NegBinomRegDETest

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

Project Dimensional reduction onto full dataset

Project Principal Components Analysis onto full dataset

Merge childen of a node

Merge Seurat Objects

Significant genes from a PCA

ICA Cell Embeddings Accessor Function

Determine statistical significance of PCA scores.

MetageneBicorPlot

Plot CC bicor saturation plot

Apply a ceiling and floor to all values in a matrix

PCA Gene Loadings Accessor Function

Principal component heatmap

Find cells with highest PCA scores

Print the results of a PCA analysis

Print PCA Calculation Parameters

Run diffusion map

Run Independent Component Analysis on gene expression

Perform Canonical Correlation Analysis with more than two groups

Run Principal Component Analysis on gene expression using IRLBA

Visualize PCA genes

Quickly Pick Relevant PCs

Print CCA Calculation Parameters

PrintCalcParams

Print the calculation

Print the results of a ICA analysis

Print ICA Calculation Parameters

SetDimReduction

Dimensional Reduction Mutator Function

Set identity class information

Differential expression testing using Tobit models

UpdateSeuratObject

Update old Seurat object to accomodate new features

ICA Gene Loadings Accessor Function

Differential expression using MAST

Make object sparse

FindAllMarkersNode

Find all markers for a node

Print the results of a dimensional reduction analysis

PrintFindClustersParams

Print FindClusters Calculation Parameters

Quantitative refinement of spatial inferences

RemoveFromTable

Remove data from a table

Run t-distributed Stochastic Neighbor Embedding

Split Dot plot visualization

Gene expression heatmap

PCA Cell Embeddings Accessor Function

PrintCalcVarExpRatioParams

Print Parameters Associated with CalcVarExpRatio

Single cell ridge plot

Perform Canonical Correlation Analysis

Save cluster assignments to a TSV file

Assay Data Mutator Function

Set Cluster Assignments

Return a subset of rows for a matrix or data frame

Print Diffusion Map Calculation Parameters

Print SNN Construction Calculation Parameters

PrintTSNEParams

Print TSNE Calculation Parameters

PurpleAndYellow

A purple and yellow color palette

Load in data from 10X

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

Switch identity class definition to another variable

Seurat-deprecated

Deprecated function(s) in the Seurat package

Shuffle a vector

show method for seurat

Scale and center the data.

Return a subset of columns for a matrix or data frame

Return a subset of the Seurat object

ValidateClusters

Cluster Validation

ValidateSpecificClusters

Specific Cluster Validation

Single cell violin plot

Identify cells matching certain criteria

A small example version of the PBMC dataset

Set identity class information

VariableGenePlot

View variable genes

VizDimReduction

Visualize Dimensional Reduction genes

Differential expression using Wilcoxon Rank Sum

Cell cycle genes

The Seurat Class