Introduction to scClassifR

Vy Nguyen

2021-10-10

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:

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)
#> Loading required package: Seurat
#> Attaching SeuratObject
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
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The models are stored in the default_models object:

data("default_models")
names(default_models)
#> [1] "B cells" "T cells" "NK"

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']]
#> An object of class scClassifR for B cells 
#> * 31 features applied: CD38, CD79B, CD74, CD84, RASGRP2, TCF3, SP140, MEF2C, DERL3, CD37, CD79A, POU2AF1, MVK, CD83, BACH2, LY86, CD86, SDC1, CR2, LRMP, VPREB3, IL2RA, BLK, IRF8, FLI1, MS4A1, CD14, MZB1, PTEN, CD19, MME 
#> * Predicting probability threshold: 0.5 
#> * No parent model

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
#> 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')

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)]
#>                                            B_cells_p B_cells_class      NK_p
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb       0.007968287            no 0.4776882
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb 0.999938756           yes 0.5227294
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb 0.995998043           yes 0.4193178
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb 0.998736176           yes 0.4623257
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb 0.999724425           yes 0.5028934
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb       0.996084449           yes 0.3515119
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb       0.017356301            no 0.4776076
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb 0.112374511            no 0.4697555
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb       0.038732890            no 0.4372554
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb       0.043200589            no 0.4639698
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb       0.989798761           yes 0.4548053
#>                                          NK_class T_cells_p T_cells_class
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb             no 0.9297574           yes
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb      yes 0.1234063            no
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb       no 0.1024498            no
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb       no 0.2495409            no
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb      yes 0.1393775            no
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb             no 0.2489775            no
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb             no 0.9393191           yes
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb       no 0.9155632           yes
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb             no 0.9615349           yes
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb             no 0.9426831           yes
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb             no 0.9244885           yes
#>                                          predicted_cell_type
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb                   T cells
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb          B cells/NK
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb             B cells
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb             B cells
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb          B cells/NK
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb                   B cells
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb                   T cells
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb             T cells
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb                   T cells
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb                   T cells
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb           B cells/T cells
#>                                          most_probable_cell_type     clust_pred
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb                       T cells 86.42% T cells
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb                 B cells   100% B cells
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb                 B cells   100% B cells
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb                 B cells   100% B cells
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb                 B cells   100% B cells
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb                       B cells   100% B cells
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb                       T cells 86.42% T cells
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb                 T cells 86.42% T cells
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb                       T cells 86.42% T cells
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb                       T cells 86.42% T cells
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb                       B cells   100% B cells

New columns are:

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()
#> R version 4.1.1 (2021-08-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server x64 (build 17763)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=C                          
#> [2] LC_CTYPE=English_United States.1252   
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C                          
#> [5] LC_TIME=English_United States.1252    
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] scClassifR_1.1.2            SingleCellExperiment_1.15.2
#>  [3] SummarizedExperiment_1.23.5 Biobase_2.53.0             
#>  [5] GenomicRanges_1.45.0        GenomeInfoDb_1.29.8        
#>  [7] IRanges_2.27.2              S4Vectors_0.31.5           
#>  [9] BiocGenerics_0.39.2         MatrixGenerics_1.5.4       
#> [11] matrixStats_0.61.0          SeuratObject_4.0.2         
#> [13] Seurat_4.0.4               
#> 
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#>  [10] htmltools_0.5.2        fansi_0.5.0            magrittr_2.0.1        
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#>  [16] recipes_0.1.17         globals_0.14.0         gower_0.2.2           
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#>  [22] xfun_0.26              dplyr_1.0.7            crayon_1.4.1          
#>  [25] RCurl_1.98-1.5         jsonlite_1.7.2         spatstat.data_2.1-0   
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#>  [67] caret_6.0-90           labeling_0.4.2         tidyselect_1.1.1      
#>  [70] rlang_0.4.11           reshape2_1.4.4         later_1.3.0           
#>  [73] munsell_0.5.0          tools_4.1.1            generics_0.1.0        
#>  [76] ggridges_0.5.3         evaluate_0.14          stringr_1.4.0         
#>  [79] fastmap_1.1.0          yaml_2.2.1             goftest_1.2-3         
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#>  [88] future_1.22.1          nlme_3.1-153           mime_0.12             
#>  [91] compiler_4.1.1         plotly_4.10.0          png_0.1-7             
#>  [94] e1071_1.7-9            spatstat.utils_2.2-0   tibble_3.1.5          
#>  [97] bslib_0.3.1            stringi_1.7.5          highr_0.9             
#> [100] lattice_0.20-45        Matrix_1.3-4           vctrs_0.3.8           
#> [103] pillar_1.6.3           lifecycle_1.0.1        spatstat.geom_2.3-0   
#> [106] lmtest_0.9-38          jquerylib_0.1.4        RcppAnnoy_0.0.19      
#> [109] data.table_1.14.2      cowplot_1.1.1          bitops_1.0-7          
#> [112] irlba_2.3.3            httpuv_1.6.3           patchwork_1.1.1       
#> [115] R6_2.5.1               promises_1.2.0.1       KernSmooth_2.23-20    
#> [118] gridExtra_2.3          parallelly_1.28.1      codetools_0.2-18      
#> [121] MASS_7.3-54            assertthat_0.2.1       withr_2.4.2           
#> [124] sctransform_0.3.2      GenomeInfoDbData_1.2.7 mgcv_1.8-38           
#> [127] parallel_4.1.1         grid_4.1.1             rpart_4.1-15          
#> [130] timeDate_3043.102      class_7.3-19           tidyr_1.1.4           
#> [133] rmarkdown_2.11         Rtsne_0.15             pROC_1.18.0           
#> [136] lubridate_1.8.0        shiny_1.7.1