Introduction to 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
#> An object of class Seurat 
#> 78 features across 480 samples within 1 assay 
#> Active assay: RNA (78 features, 34 variable features)
#>  1 dimensional reduction calculated: umap

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[[]])
#>                               orig.ident nCount_RNA nFeature_RNA percent.mt
#> Cy80_II_CD45_B07_S883_comb SeuratProject   42.46011            8          0
#> Cy80_II_CD45_C09_S897_comb SeuratProject   74.35907           14          0
#> Cy80_II_CD45_H07_S955_comb SeuratProject   42.45392            8          0
#> Cy80_II_CD45_H09_S957_comb SeuratProject   63.47043           12          0
#> Cy80_II_CD45_B11_S887_comb SeuratProject   47.26798            9          0
#> Cy80_II_CD45_D11_S911_comb SeuratProject   69.12167           13          0
#>                            RNA_snn_res.0.8 seurat_clusters RNA_snn_res.0.5
#> Cy80_II_CD45_B07_S883_comb               4               4               2
#> Cy80_II_CD45_C09_S897_comb               4               4               2
#> Cy80_II_CD45_H07_S955_comb               4               4               2
#> Cy80_II_CD45_H09_S957_comb               4               4               2
#> Cy80_II_CD45_B11_S887_comb               4               4               2
#> Cy80_II_CD45_D11_S911_comb               1               1               1

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')

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.