Seurat

The Seurat object ...

Slots

Slot	Function
assays	A list of assays within this object
meta.data	Cell-level meta data
active.assay	Name of active, or default, assay
active.ident	Identity classes for the current object
graphs	A list of nearest neighbor graphs
reductions	A list of DimReduc objects
project.name	User-defined project name (optional)
tools	Empty list. Tool developers can store any internal data from their methods here
misc	Empty slot. User can store additional information here
version	Seurat version used when creating the object

Object Information

Summary information about Seurat objects can be had quickly and easily using standard R functions. Object shape/dimensions can be found using the dim, ncol, and nrow functions; cell and feature names can be found using the colnames and rownames functions, respectively, or the dimnames function. A vector of names of Assay, DimReduc, and Graph objects contained in a Seurat object can be had by using names.

# The following examples use the PBMC 3k dataset
> pbmc
An object of class Seurat
 13714 features across 2638 samples within 1 assay
 2 dimensional reductions calculated: pca, tsne
# nrow and ncol provide the number of features and cells in the active assay, respectively
# dim provides both nrow and ncol at the same time
> dim(x = pbmc)
[1] 13714  2638
# In addtion to rownames and colnames, one can use dimnames
# which provides a two-length list with both rownames and colnames
> head(x = rownames(x = pbmc))
[1] "AL627309.1"    "AP006222.2"    "RP11-206L10.2" "RP11-206L10.9"
[5] "LINC00115"     "NOC2L"
> head(x = colnames(x = pbmc))
[1] "AAACATACAACCAC" "AAACATTGAGCTAC" "AAACATTGATCAGC" "AAACCGTGCTTCCG"
[5] "AAACCGTGTATGCG" "AAACGCACTGGTAC"
# A vector of names of associated objects can be had with the names function
# These can be passed to the double [[ extract operator to pull them from the Seurat object
> names(x = pbmc)
[1] "RNA"  "pca"  "tsne"

Data Access

Pulling specific Assay, DimReduc, or Graph objects can be done with the double [[ extract operator. Adding new objects to a Seurat object is also done with the double [[ extract operator; Seurat will figure out where in the Seurat object a new associated object belongs.

> pbmc[['RNA']]
Assay data with 13714 features for 2638 cells
Top 10 variable features:
 PPBP, DOK3, NFE2L2, ARVCF, YPEL2, UBE2D4, FAM210B, CTB-113I20.2, GBGT1,
 GMPPA
> pbmc[['tsne']]
A dimensional reduction object with key tSNE_
 Number of dimensions: 2
 Projected dimensional reduction calculated: FALSE
 Jackstraw run: FALSE

Accessing data from an Seurat object is done with the GetAssayData function. Adding expression data to either the counts, data, or scale.data slots can be done with SetAssayData. New data must have the same cells in the same order as the current expression data. Data added to counts or data must have the same features as the current expression data.

> GetAssayData(object = pbmc, slot = 'scale.data')[1:3, 1:3]
              AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC
AL627309.1       -0.06547546    -0.10052277    -0.05804007
AP006222.2       -0.02690776    -0.02820169    -0.04508318
RP11-206L10.2    -0.03596234    -0.17689415    -0.09997719
# SetAssayData example...

Cell-level meta data can be accessed with the single [[ extract operator or using the $ sigil. Pulling with the $ sigil means only one bit of meta data can be pulled at a time, though tab-autocompletion has been enabled for it, making it ideal for interactive use. Adding cell-level meta data can be set using the single [[ extract operator as well, or by using AddMetaData.

# Cell-level meta data is stored as a data frame
# Standard data frame functions work on the meta data data frame
> colnames(x = pbmc[[]])
[1] "nGene"        "nUMI"         "orig.ident"   "percent.mito" "res.0.6"
# One can pull multiple values from the data frame at any time
> head(x = pbmc[[c('nUMI', 'percent.mito')]])
               nUMI percent.mito
AAACATACAACCAC 2421  0.030177759
AAACATTGAGCTAC 4903  0.037935958
AAACATTGATCAGC 3149  0.008897363
AAACCGTGCTTCCG 2639  0.017430845
AAACCGTGTATGCG  981  0.012244898
AAACGCACTGGTAC 2164  0.016643551
# The $ sigil can only pull bit of meta data at a time; however, tab-autocompletion
# has been enabled for the $ sigil, making it ideal for interactive use
> head(x = pbmc$percent.mito)
               percent.mito
AAACATACAACCAC  0.030177759
AAACATTGAGCTAC  0.037935958
AAACATTGATCAGC  0.008897363
AAACCGTGCTTCCG  0.017430845
AAACCGTGTATGCG  0.012244898
AAACGCACTGGTAC  0.016643551
# Passing `drop = TRUE` will turn the meta data into a names vector
# with each entry being named for the cell it corresponds to
> head(x = pbmc[['res.0.6', drop = TRUE]])
AAACATACAACCAC AAACATTGAGCTAC AAACATTGATCAGC AAACCGTGCTTCCG AAACCGTGTATGCG
           "0"            "2"            "0"            "1"            "5"
AAACGCACTGGTAC
           "0"
# Add meta data example

The HVFInfo function pulls feature mean and dispersion from an Assay object. The vector of variable features can be pulled with the VariableFeatures function. VariableFeatures can also set the vector of variable features.

# HVFInfo pulls mean, dispersion, and dispersion scaled
# Useful for viewing the results of FindVariableFeatures
> head(x = HVFInfo(object = pbmc))
                     mean dispersion dispersion.scaled
AL627309.1    0.013555659   1.432845        -0.6236875
AP006222.2    0.004695980   1.458631        -0.5728009
RP11-206L10.2 0.005672517   1.325459        -0.8356099
RP11-206L10.9 0.002644177   0.859264        -1.7556304
LINC00115     0.027437275   1.457477        -0.5750770
NOC2L         0.376037723   1.876440        -0.4162432
# VariableFeatures both accesses and sets the vector of variable features
> head(x = VariableFeatures(object = pbmc))
[1] "PPBP"   "DOK3"   "NFE2L2" "ARVCF"  "YPEL2"  "UBE2D4"
# Set variable features example

A vector of standard deviations for a DimReduc stored within the Seurat object can be found with Stdev.

> Stdev(object = pbmc, reduction.use = 'pca')
 [1] 5.666584 4.326466 3.952192 3.638124 2.191529 1.996551 1.877891 1.798251
 [9] 1.766873 1.753684 1.731568 1.720525 1.718079 1.715879 1.707009 1.702660
[17] 1.697318 1.692549 1.686149 1.683967

Methods

Methods for the Seurat class can be found with the following:

library(Seurat)
utils::methods(class = 'Seurat')

• [
• [[
• [[<-
• colMeans
• colSums
• Command
• DefaultAssay
• DefaultAssay<-
• dimnames
• dim
• GetAssayData
• GetAssay
• HVFInfo
• Idents
• Idents<-
• merge
• names
• SetAssayData
• Stdev
• subset
• SubsetData
• VariableFeatures
• VariableFeatures<-
• WhichCells