Introduction to scAnnotatR

Vy Nguyen

2024-05-01

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
#> Loading required package: SeuratObject
#> Loading required package: sp
#> 
#> Attaching package: 'SeuratObject'
#> The following object is masked from 'package:base':
#> 
#>     intersect
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: 'BiocGenerics'
#> The following object is masked from 'package:SeuratObject':
#> 
#>     intersect
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
#>     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
#>     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
#> 
#>     findMatches
#> The following objects are masked from 'package:base':
#> 
#>     expand.grid, I, unname
#> Loading required package: IRanges
#> 
#> Attaching package: 'IRanges'
#> The following object is masked from 'package:sp':
#> 
#>     %over%
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: 'Biobase'
#> The following object is masked from 'package:MatrixGenerics':
#> 
#>     rowMedians
#> The following objects are masked from 'package:matrixStats':
#> 
#>     anyMissing, rowMedians
#> 
#> Attaching package: 'SummarizedExperiment'
#> The following object is masked from 'package:Seurat':
#> 
#>     Assays
#> The following object is masked from 'package:SeuratObject':
#> 
#>     Assays
#> Warning: replacing previous import 'ape::where' by 'dplyr::where' when loading
#> 'scAnnotatR'

The models are stored in the default_models object:

default_models <- load_models("default")
#> 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 
#> 91 features across 480 samples within 1 assay 
#> Active assay: RNA (91 features, 0 variable features)
#>  2 layers present: counts, data
#>  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')
#> 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 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] scAnnotatR_1.10.0           SingleCellExperiment_1.26.0
#>  [3] SummarizedExperiment_1.34.0 Biobase_2.64.0             
#>  [5] GenomicRanges_1.56.0        GenomeInfoDb_1.40.0        
#>  [7] IRanges_2.38.0              S4Vectors_0.42.0           
#>  [9] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
#> [11] matrixStats_1.3.0           Seurat_5.0.3               
#> [13] SeuratObject_5.0.1          sp_2.1-4                   
#> 
#> loaded via a namespace (and not attached):
#>   [1] RcppAnnoy_0.0.22        splines_4.4.0           later_1.3.2            
#>   [4] filelock_1.0.3          tibble_3.2.1            polyclip_1.10-6        
#>   [7] hardhat_1.3.1           pROC_1.18.5             rpart_4.1.23           
#>  [10] fastDummies_1.7.3       lifecycle_1.0.4         globals_0.16.3         
#>  [13] lattice_0.22-6          MASS_7.3-60.2           magrittr_2.0.3         
#>  [16] plotly_4.10.4           sass_0.4.9              rmarkdown_2.26         
#>  [19] jquerylib_0.1.4         yaml_2.3.8              httpuv_1.6.15          
#>  [22] sctransform_0.4.1       spam_2.10-0             spatstat.sparse_3.0-3  
#>  [25] reticulate_1.36.1       cowplot_1.1.3           pbapply_1.7-2          
#>  [28] DBI_1.2.2               RColorBrewer_1.1-3      lubridate_1.9.3        
#>  [31] abind_1.4-5             zlibbioc_1.50.0         Rtsne_0.17             
#>  [34] purrr_1.0.2             nnet_7.3-19             rappdirs_0.3.3         
#>  [37] ipred_0.9-14            lava_1.8.0              GenomeInfoDbData_1.2.12
#>  [40] data.tree_1.1.0         ggrepel_0.9.5           irlba_2.3.5.1          
#>  [43] listenv_0.9.1           spatstat.utils_3.0-4    goftest_1.2-3          
#>  [46] RSpectra_0.16-1         spatstat.random_3.2-3   fitdistrplus_1.1-11    
#>  [49] parallelly_1.37.1       leiden_0.4.3.1          codetools_0.2-20       
#>  [52] DelayedArray_0.30.0     tidyselect_1.2.1        farver_2.1.1           
#>  [55] UCSC.utils_1.0.0        BiocFileCache_2.12.0    spatstat.explore_3.2-7 
#>  [58] jsonlite_1.8.8          caret_6.0-94            e1071_1.7-14           
#>  [61] progressr_0.14.0        ggridges_0.5.6          survival_3.6-4         
#>  [64] iterators_1.0.14        foreach_1.5.2           tools_4.4.0            
#>  [67] ica_1.0-3               Rcpp_1.0.12             glue_1.7.0             
#>  [70] prodlim_2023.08.28      gridExtra_2.3           SparseArray_1.4.0      
#>  [73] xfun_0.43               dplyr_1.1.4             withr_3.0.0            
#>  [76] BiocManager_1.30.22     fastmap_1.1.1           fansi_1.0.6            
#>  [79] digest_0.6.35           timechange_0.3.0        R6_2.5.1               
#>  [82] mime_0.12               colorspace_2.1-0        scattermore_1.2        
#>  [85] tensor_1.5              spatstat.data_3.0-4     RSQLite_2.3.6          
#>  [88] utf8_1.2.4              tidyr_1.3.1             generics_0.1.3         
#>  [91] data.table_1.15.4       recipes_1.0.10          class_7.3-22           
#>  [94] httr_1.4.7              htmlwidgets_1.6.4       S4Arrays_1.4.0         
#>  [97] ModelMetrics_1.2.2.2    uwot_0.2.2              pkgconfig_2.0.3        
#> [100] gtable_0.3.5            timeDate_4032.109       blob_1.2.4             
#> [103] lmtest_0.9-40           XVector_0.44.0          htmltools_0.5.8.1      
#> [106] dotCall64_1.1-1         scales_1.3.0            png_0.1-8              
#> [109] gower_1.0.1             knitr_1.46              reshape2_1.4.4         
#> [112] nlme_3.1-164            curl_5.2.1              proxy_0.4-27           
#> [115] cachem_1.0.8            zoo_1.8-12              stringr_1.5.1          
#> [118] BiocVersion_3.19.1      KernSmooth_2.23-22      parallel_4.4.0         
#> [121] miniUI_0.1.1.1          AnnotationDbi_1.66.0    pillar_1.9.0           
#> [124] grid_4.4.0              vctrs_0.6.5             RANN_2.6.1             
#> [127] promises_1.3.0          dbplyr_2.5.0            xtable_1.8-4           
#> [130] cluster_2.1.6           evaluate_0.23           cli_3.6.2              
#> [133] compiler_4.4.0          rlang_1.1.3             crayon_1.5.2           
#> [136] future.apply_1.11.2     labeling_0.4.3          plyr_1.8.9             
#> [139] stringi_1.8.3           viridisLite_0.4.2       deldir_2.0-4           
#> [142] munsell_0.5.1           Biostrings_2.72.0       lazyeval_0.2.2         
#> [145] spatstat.geom_3.2-9     Matrix_1.7-0            RcppHNSW_0.6.0         
#> [148] patchwork_1.2.0         bit64_4.0.5             future_1.33.2          
#> [151] ggplot2_3.5.1           KEGGREST_1.44.0         shiny_1.8.1.1          
#> [154] highr_0.10              AnnotationHub_3.12.0    kernlab_0.9-32         
#> [157] ROCR_1.0-11             igraph_2.0.3            memoise_2.0.1          
#> [160] bslib_0.7.0             bit_4.0.5               ape_5.8