Introduction to scAnnotatR

Vy Nguyen

2021-10-26

Introduction

scAnnotatR 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/

scAnnotatR provides 2 main features:

Installation

The scAnnotatR package can be directly installed from Bioconductor:

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

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

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

Included models

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

# load scAnnotatR into working space
library(scAnnotatR)
#> Loading required package: Seurat
#> Attaching SeuratObject
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
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#> Loading required package: matrixStats
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#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
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#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
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The models are stored in the default_models object:

default_models <- load_models("default")
#> snapshotDate(): 2021-10-20
#> loading from cache
names(default_models)
#>  [1] "B cells"           "Plasma cells"      "NK"               
#>  [4] "CD16 NK"           "CD56 NK"           "T cells"          
#>  [7] "CD4 T cells"       "CD8 T cells"       "Treg"             
#> [10] "NKT"               "ILC"               "Monocytes"        
#> [13] "CD14 Mono"         "CD16 Mono"         "DC"               
#> [16] "pDC"               "Endothelial cells" "LEC"              
#> [19] "VEC"               "Platelets"         "RBC"              
#> [22] "Melanocyte"        "Schwann cells"     "Pericytes"        
#> [25] "Mast cells"        "Keratinocytes"     "alpha"            
#> [28] "beta"              "delta"             "gamma"            
#> [31] "acinar"            "ductal"            "Fibroblasts"

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

default_models[['B cells']]
#> An object of class scAnnotatR for B cells 
#> * 31 marker genes 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 scAnnotatR

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, 
                             assay = 'RNA', slot = 'counts',
                             cell_types = c('B cells', 'NK', 'T cells'), 
                             path_to_models = 'default')
#> snapshotDate(): 2021-10-20
#> loading from cache

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:ncol(seurat.obj[[]]))]
#>                                            B_cells_p B_cells_class      NK_p
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb       0.007754246            no 0.4881285
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb 0.999385770           yes 0.4440553
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb 0.998317662           yes 0.4416114
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb 0.997774856           yes 0.4398997
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb 0.998874031           yes 0.4541005
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb       0.999944282           yes 0.4511450
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb       0.015978230            no 0.4841041
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb 0.099311534            no 0.4858084
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb       0.055754074            no 0.4924746
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb       0.048558881            no 0.5002238
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb       0.996979702           yes 0.4994867
#>                                          NK_class  T_cells_p T_cells_class
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb             no 0.94205232           yes
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb       no 0.11269306            no
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb       no 0.09834696            no
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb       no 0.22256938            no
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb       no 0.12903487            no
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb             no 0.27242536            no
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb             no 0.94929624           yes
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb       no 0.93390248           yes
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb             no 0.98161289           yes
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb            yes 0.96436674           yes
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb             no 0.94848597           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
#> 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
#> 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                NK/T cells
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb           B cells/T cells
#>                                          most_probable_cell_type
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb                       T cells
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb                 B cells
#> 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
#> 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

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 scAnnotatR 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 scAnnotatR.

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] scAnnotatR_1.0.0            SingleCellExperiment_1.16.0
#>  [3] SummarizedExperiment_1.24.0 Biobase_2.54.0             
#>  [5] GenomicRanges_1.46.0        GenomeInfoDb_1.30.0        
#>  [7] IRanges_2.28.0              S4Vectors_0.32.0           
#>  [9] BiocGenerics_0.40.0         MatrixGenerics_1.6.0       
#> [11] matrixStats_0.61.0          SeuratObject_4.0.2         
#> [13] Seurat_4.0.5               
#> 
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#>   [1] utf8_1.2.2                    reticulate_1.22              
#>   [3] tidyselect_1.1.1              RSQLite_2.2.8                
#>   [5] AnnotationDbi_1.56.0          htmlwidgets_1.5.4            
#>   [7] grid_4.1.1                    Rtsne_0.15                   
#>   [9] pROC_1.18.0                   munsell_0.5.0                
#>  [11] codetools_0.2-18              ica_1.0-2                    
#>  [13] future_1.22.1                 miniUI_0.1.1.1               
#>  [15] withr_2.4.2                   colorspace_2.0-2             
#>  [17] filelock_1.0.2                highr_0.9                    
#>  [19] knitr_1.36                    ROCR_1.0-11                  
#>  [21] tensor_1.5                    listenv_0.8.0                
#>  [23] labeling_0.4.2                GenomeInfoDbData_1.2.7       
#>  [25] polyclip_1.10-0               bit64_4.0.5                  
#>  [27] farver_2.1.0                  parallelly_1.28.1            
#>  [29] vctrs_0.3.8                   generics_0.1.1               
#>  [31] ipred_0.9-12                  xfun_0.27                    
#>  [33] BiocFileCache_2.2.0           R6_2.5.1                     
#>  [35] bitops_1.0-7                  spatstat.utils_2.2-0         
#>  [37] cachem_1.0.6                  DelayedArray_0.20.0          
#>  [39] assertthat_0.2.1              promises_1.2.0.1             
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#>  [43] gtable_0.3.0                  globals_0.14.0               
#>  [45] goftest_1.2-3                 timeDate_3043.102            
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#>  [49] lazyeval_0.2.2                ModelMetrics_1.2.2.2         
#>  [51] spatstat.geom_2.3-0           BiocManager_1.30.16          
#>  [53] yaml_2.2.1                    reshape2_1.4.4               
#>  [55] abind_1.4-5                   httpuv_1.6.3                 
#>  [57] caret_6.0-90                  tools_4.1.1                  
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#>  [87] evaluate_0.14                 xtable_1.8-4                 
#>  [89] gridExtra_2.3                 compiler_4.1.1               
#>  [91] tibble_3.1.5                  KernSmooth_2.23-20           
#>  [93] crayon_1.4.1                  htmltools_0.5.2              
#>  [95] mgcv_1.8-38                   later_1.3.0                  
#>  [97] tidyr_1.1.4                   lubridate_1.8.0              
#>  [99] DBI_1.1.1                     dbplyr_2.1.1                 
#> [101] MASS_7.3-54                   rappdirs_0.3.3               
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#> [111] recipes_0.1.17                foreach_1.5.1                
#> [113] bslib_0.3.1                   XVector_0.34.0               
#> [115] prodlim_2019.11.13            stringr_1.4.0                
#> [117] digest_0.6.28                 sctransform_0.3.2            
#> [119] RcppAnnoy_0.0.19              spatstat.data_2.1-0          
#> [121] Biostrings_2.62.0             rmarkdown_2.11               
#> [123] leiden_0.3.9                  uwot_0.1.10                  
#> [125] curl_4.3.2                    kernlab_0.9-29               
#> [127] shiny_1.7.1                   lifecycle_1.0.1              
#> [129] nlme_3.1-153                  jsonlite_1.7.2               
#> [131] viridisLite_0.4.0             fansi_0.5.0                  
#> [133] pillar_1.6.4                  lattice_0.20-45              
#> [135] KEGGREST_1.34.0               fastmap_1.1.0                
#> [137] httr_1.4.2                    survival_3.2-13              
#> [139] interactiveDisplayBase_1.32.0 glue_1.4.2                   
#> [141] png_0.1-7                     iterators_1.0.13             
#> [143] BiocVersion_3.14.0            bit_4.0.4                    
#> [145] class_7.3-19                  stringi_1.7.5                
#> [147] sass_0.4.0                    blob_1.2.2                   
#> [149] AnnotationHub_3.2.0           memoise_2.0.0                
#> [151] dplyr_1.0.7                   irlba_2.3.3                  
#> [153] e1071_1.7-9                   future.apply_1.8.1           
#> [155] ape_5.5