In grisslab/scClassifR: Pretrained learning models for cell type prediction on single cell RNA-sequencing data

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Introduction

scClassifR is an R package for cell type prediction on single cell RNA-sequencing data. Currently, this package supports data in the forms of a Seurat object or a SingleCellExperiment object.

More information about Seurat object can be found here: https://satijalab.org/seurat/ More information about SingleCellExperiment object can be found here: https://osca.bioconductor.org/

scClassifR provides 2 main features:

• A set of pretrained and robust classifiers for basic immune cells. See the section below.
• A user-friendly and fully customizable framework to train new classification models. These models can then be easily saved and reused in the future. Details usage of this framework is explained in vignettes 2 and 3.

Installation

The scClassifR package can be directly installed from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

if (!require(scClassifR))
  BiocManager::install("scClassifR")

For more information, see https://bioconductor.org/install/.

Included models

The scClassifR package comes with several pre-trained models to classify cell types.

# load scClassifR into working space
library(scClassifR)

The models are stored in the default_models object:

data("default_models")
names(default_models)

The default_models object is named a list of classifiers. Each classifier is an instance of the scClassifR S4 class. For example:

default_models[['B cells']]

Basic pipeline to identify cell types in a scRNA-seq dataset using scClassifR

Preparing the data

To identify cell types available in a dataset, we need to load the dataset as Seurat or SingleCellExperiment object.

For this vignette, we use a small sample datasets that is available as a Seurat object as part of the package.

# load the example dataset
data("tirosh_mel80_example")
tirosh_mel80_example

The example dataset already contains the clustering results as part of the metadata. This is not necessary for the classification process.

head(tirosh_mel80_example[[]])

Cell classification

To launch cell type identification, we simply call the classify_cells function. A detailed description of all parameters can be found through the function's help page ?classify_cells.

Here we use only 3 classifiers for B cells, T cells and NK cells to reduce computational cost of this vignette. If users want to use all pretrained classifiers on their dataset, cell_types = 'all' can be used.

seurat.obj <- classify_cells(classify_obj = tirosh_mel80_example, 
                             seurat_assay = 'RNA', seurat_slot = 'data',
                             cell_types = c('B cells', 'NK', 'T cells'), 
                             path_to_models = 'default')

Parameters

• The option cell_types = 'all' tells the function to use all available cell classification models. Alternatively, we can limit the identifiable cell types:

  • by specifying: cell_types = c('B cells', 'T cells')
  • or by indicating the applicable classifier using the classifiers option: classifiers = c(default_models[['B cells']], default_models[['T cells']])

• The option path_to_models = 'default' is to automatically use the package-integrated pretrained models (without loading the models into the current working space). This option can be used to load a local database instead. For more details see the vignettes on training your own classifiers.

Result interpretation

The classify_cells function returns the input object but with additional columns in the metadata table.

# display the additional metadata fields
seurat.obj[[]][c(50:60), c(8:16)]

New columns are:

• predicted_cell_type: The predicted cell type, also containing any ambiguous assignments. In these cases, the possible cell types are separated by a "/"

• most_probable_cell_type: contains the most probably cell type ignoring any ambiguous assignments.

• columns with syntax [celltype]_p: probability of a cell to belong to a cell type. Unknown cell types are marked as NAs.

Result visualization

The predicted cell types can now simply be visualized using the matching plotting functions. In this example, we use Seurat's DimPlot function:

# Visualize the cell types
Seurat::DimPlot(seurat.obj, group.by = "most_probable_cell_type")

With the current number of cell classifiers, we identify cells belonging to 2 cell types (B cells and T cells) and to 2 subtypes of T cells (CD4+ T cells and CD8+ T cells). The other cells (red points) are not among the cell types that can be classified by the predefined classifiers. Hence, they have an empty label.

For a certain cell type, users can also view the prediction probability. Here we show an example of B cell prediction probability:

# Visualize the cell types
Seurat::FeaturePlot(seurat.obj, features = "B_cells_p")

Cells predicted to be B cells with higher probability have darker color, while the lighter color shows lower or even zero probability of a cell to be B cells. For B cell classifier, the threshold for prediction probability is currently at 0.5, which means cells having prediction probability at 0.5 or above will be predicted as B cells.

The automatic cell identification by scClassifR matches the traditional cell assignment, ie. the approach based on cell canonical marker expression. Taking a simple example, we use CD19 and CD20 (MS4A1) to identify B cells:

# Visualize the cell types
Seurat::FeaturePlot(seurat.obj, features = c("CD19", "MS4A1"), ncol = 2)

We see that the marker expression of B cells exactly overlaps the B cell prediction made by scClassifR.

Session Info

sessionInfo()

grisslab/scClassifR documentation built on Oct. 27, 2021, 12:13 p.m.