16 Structure

16.1 Load an existing Seurat object

The data we’re working with today is a small dataset of about 5000 PBMCs (peripheral blood mononuclear cells) from a healthy donor.

First, load Seurat package.

library(Seurat)

And here’s the one we prepared earlier. Seurat objects are usually saved as ‘.rds’ files, which is an R format for storing binary data (not-text or human-readable). The functions readRDS() can load it.

seurat_object <- readRDS("data/kang2018.rds")

Discussion: The Seurat Object in R

Lets take a look at the seurat object we have just created in R, seurat_object

To accomodate the complexity of data arising from a single cell RNA seq experiment, the seurat object keeps this as a container of multiple data tables that are linked.

The functions in seurat can access parts of the data object for analysis and visualization, we will cover this later on.

There are a couple of concepts to discuss here.

Class

These are essentially data containers in R as a class, and can accessed as a variable in the R environment.

Classes are pre-defined and can contain multiple data tables and metadata. For Seurat, there are three types.

Seurat - the main data class, contains all the data.
Assay - found within the Seurat object. Depending on the experiment a cell could have data on RNA, ATAC etc measured
DimReduc - for PCA and UMAP

Slots

Slots are parts within a class that contain specific data. These can be lists, data tables and vectors and can be accessed with conventional R methods.

Data Access

Many of the functions in Seurat operate on the data class and slots within them seamlessly. There maybe occasion to access these separately to hack them, however this is an advanced analysis method.

The ways to access the slots can be through methods for the class (functions) or with standard R accessor nomenclature.

Examples of accessing a Seurat object

The assays slot in seurat_object can be accessed with seurat_object@assays.

The RNA assay can be accessed from this with seurat_object@assays$RNA.

We often want to access assays, so Seurat nicely gives us a shortcut seurat_object$RNA. You may sometimes see an alternative notation seurat_object[["RNA"]].

In general, slots that are always in an object are accessed with @ and things that may be different in different data sets are accessed with $.

Have a go

Use str to look at the structure of the Seurat object seurat_object.

What is in the meta.data slot within your Seurat object currently? What type of data is contained here?

Where is our count data within the Seurat object?

16.2 What’s in there?

Some of the most important information for working with Seurat objects is in the metadata. This is cell level information - each row is one cell, identified by its barcode. Extra information gets added to this table as analysis progresses.

head(seurat_object@meta.data)
#>                     orig.ident nCount_RNA nFeature_RNA  ind
#> AGGGCGCTATTTCC-1 SeuratProject       2020          523 1256
#> GGAGACGATTCGTT-1 SeuratProject        840          381 1256
#> CACCGTTGTCGTAG-1 SeuratProject       3097          995 1016
#> TATCGTACACGCAT-1 SeuratProject       1011          540 1488
#> TGACGCCTTGCTTT-1 SeuratProject        570          367  101
#> TACGAGACCTATTC-1 SeuratProject       2399          770 1244
#>                  stim              cell multiplets
#> AGGGCGCTATTTCC-1 stim   CD14+ Monocytes    singlet
#> GGAGACGATTCGTT-1 stim       CD4 T cells    singlet
#> CACCGTTGTCGTAG-1 ctrl FCGR3A+ Monocytes    singlet
#> TATCGTACACGCAT-1 stim           B cells    singlet
#> TGACGCCTTGCTTT-1 ctrl       CD4 T cells       ambs
#> TACGAGACCTATTC-1 stim       CD4 T cells    singlet

That doesn’t have any gene expression though, that’s stored in an ‘Assay’. The Assay structure has some nuances (see discussion below), but there are functions that get the assay data out for you.

By default this object will return the normalised data (from the only assay in this object, called RNA). Every ‘.’ is a zero.

GetAssayData(seurat_object)[1:15,1:2]
#> 15 x 2 sparse Matrix of class "dgCMatrix"
#>               AGGGCGCTATTTCC-1 GGAGACGATTCGTT-1
#> MIR1302-10                   .                .
#> FAM138A                      .                .
#> OR4F5                        .                .
#> RP11-34P13.7                 .                .
#> RP11-34P13.8                 .                .
#> AL627309.1                   .                .
#> RP11-34P13.14                .                .
#> RP11-34P13.9                 .                .
#> AP006222.2                   .                .
#> RP4-669L17.10                .                .
#> OR4F29                       .                .
#> RP4-669L17.2                 .                .
#> RP5-857K21.15                .                .
#> RP5-857K21.1                 .                .
#> RP5-857K21.2                 .                .

But the raw counts data is accessible too.

GetAssayData(seurat_object, slot='counts')[1:15,1:2]
#> 15 x 2 sparse Matrix of class "dgCMatrix"
#>               AGGGCGCTATTTCC-1 GGAGACGATTCGTT-1
#> MIR1302-10                   .                .
#> FAM138A                      .                .
#> OR4F5                        .                .
#> RP11-34P13.7                 .                .
#> RP11-34P13.8                 .                .
#> AL627309.1                   .                .
#> RP11-34P13.14                .                .
#> RP11-34P13.9                 .                .
#> AP006222.2                   .                .
#> RP4-669L17.10                .                .
#> OR4F29                       .                .
#> RP4-669L17.2                 .                .
#> RP5-857K21.15                .                .
#> RP5-857K21.1                 .                .
#> RP5-857K21.2                 .                .

15 Resources

17 Acknowledgements