1 Introduction

A common application of single-cell RNA sequencing (RNA-seq) data is to identify discrete cell types. To take advantage of the large collection of well-annotated scRNA-seq datasets, scClassify package implements a set of methods to perform accurate cell type classification based on ensemble learning and sample size calculation.

This vignette will provide an example showing how users can use a pretrained model of scClassify to predict cell types. A pretrained model is a scClassifyTrainModel object returned by train_scClassify(). A list of pretrained model can be found in https://sydneybiox.github.io/scClassify/index.html.

First, install scClassify, install BiocManager and use BiocManager::install to install scClassify package.

# installation of scClassify
if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("scClassify")

2 Setting up the data

We assume that you have log-transformed (size-factor normalized) matrices as query datasets, where each row refers to a gene and each column a cell. For demonstration purposes, we will take a subset of single-cell pancreas datasets from one independent study (Wang et al.).

library(scClassify)
data("scClassify_example")
wang_cellTypes <- scClassify_example$wang_cellTypes
exprsMat_wang_subset <- scClassify_example$exprsMat_wang_subset
exprsMat_wang_subset <- as(exprsMat_wang_subset, "dgCMatrix")

Here, we load our pretrained model using a subset of the Xin et al.  human pancreas dataset as our reference data.

First, let us check basic information relating to our pretrained model.

data("trainClassExample_xin")
trainClassExample_xin
#> Class: scClassifyTrainModel 
#> Model name: training 
#> Feature selection methods: limma 
#> Number of cells in the training data: 674 
#> Number of cell types in the training data: 4

In this pretrained model, we have selected the genes based on Differential Expression using limma. To check the genes that are available in the pretrained model:

features(trainClassExample_xin)
#> [1] "limma"

We can also visualise the cell type tree of the reference data.

plotCellTypeTree(cellTypeTree(trainClassExample_xin))

3 Running scClassify

Next, we perform predict_scClassify with our pretrained model trainRes = trainClassExample to predict the cell types of our query data matrix exprsMat_wang_subset_sparse. Here, we used pearson and spearman as similarity metrics.

pred_res <- predict_scClassify(exprsMat_test = exprsMat_wang_subset,
                               trainRes = trainClassExample_xin,
                               cellTypes_test = wang_cellTypes,
                               algorithm = "WKNN",
                               features = c("limma"),
                               similarity = c("pearson", "spearman"),
                               prob_threshold = 0.7,
                               verbose = TRUE)
#> Performing unweighted ensemble learning... 
#> Using parameters: 
#> similarity  algorithm   features 
#>  "pearson"     "WKNN"    "limma" 
#> [1] "Using dynamic correlation cutoff..."
#> [1] "Using dynamic correlation cutoff..."
#> classify_res
#>                correct   correctly unassigned           intermediate 
#>            0.704590818            0.239520958            0.000000000 
#> incorrectly unassigned         error assigned          misclassified 
#>            0.000000000            0.051896208            0.003992016 
#> Using parameters: 
#> similarity  algorithm   features 
#> "spearman"     "WKNN"    "limma" 
#> [1] "Using dynamic correlation cutoff..."
#> [1] "Using dynamic correlation cutoff..."
#> classify_res
#>                correct   correctly unassigned           intermediate 
#>            0.702594810            0.013972056            0.000000000 
#> incorrectly unassigned         error assigned          misclassified 
#>            0.001996008            0.277445110            0.003992016 
#> weights for each base method: 
#> [1] NA NA

Noted that the cellType_test is not a required input. For datasets with unknown labels, users can simply leave it as cellType_test = NULL.

Prediction results for pearson as the similarity metric:

table(pred_res$pearson_WKNN_limma$predRes, wang_cellTypes)
#>                   wang_cellTypes
#>                    acinar alpha beta delta ductal gamma stellate
#>   alpha                 0   206    0     0      0     2        0
#>   beta                  0     0  118     0      1     0        0
#>   beta_delta_gamma      0     0    0     0     25     0        0
#>   delta                 0     0    0    10      0     0        0
#>   gamma                 0     0    0     0      0    19        0
#>   unassigned            5     0    0     0     70     0       45

Prediction results for spearman as the similarity metric:

table(pred_res$spearman_WKNN_limma$predRes, wang_cellTypes)
#>                   wang_cellTypes
#>                    acinar alpha beta delta ductal gamma stellate
#>   alpha                 0   206    0     0      0     2        2
#>   beta                  2     0  118     0     29     0        6
#>   beta_delta_gamma      1     0    0     0     66     0       31
#>   delta                 0     0    0    10      0     0        2
#>   gamma                 0     0    0     0      0    18        0
#>   unassigned            2     0    0     0      1     1        4

4 Session Info

sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.6.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] scClassify_1.14.0 BiocStyle_2.30.0 
#> 
#> loaded via a namespace (and not attached):
#>   [1] bitops_1.0-7                gridExtra_2.3              
#>   [3] rlang_1.1.1                 magrittr_2.0.3             
#>   [5] matrixStats_1.0.0           compiler_4.3.1             
#>   [7] mgcv_1.9-0                  DelayedMatrixStats_1.24.0  
#>   [9] vctrs_0.6.3                 reshape2_1.4.4             
#>  [11] stringr_1.5.0               pkgconfig_2.0.3            
#>  [13] crayon_1.5.2                fastmap_1.1.1              
#>  [15] magick_2.7.4                XVector_0.42.0             
#>  [17] labeling_0.4.2              ggraph_2.1.0               
#>  [19] utf8_1.2.3                  rmarkdown_2.23             
#>  [21] purrr_1.0.1                 xfun_0.39                  
#>  [23] zlibbioc_1.48.0             cachem_1.0.8               
#>  [25] GenomeInfoDb_1.38.0         jsonlite_1.8.7             
#>  [27] highr_0.10                  rhdf5filters_1.14.0        
#>  [29] DelayedArray_0.28.0         Rhdf5lib_1.24.0            
#>  [31] BiocParallel_1.36.0         tweenr_2.0.2               
#>  [33] parallel_4.3.1              cluster_2.1.4              
#>  [35] R6_2.5.1                    bslib_0.5.0                
#>  [37] stringi_1.7.12              limma_3.58.0               
#>  [39] diptest_0.76-0              GenomicRanges_1.54.0       
#>  [41] jquerylib_0.1.4             Rcpp_1.0.11                
#>  [43] bookdown_0.34               SummarizedExperiment_1.32.0
#>  [45] knitr_1.43                  mixtools_2.0.0             
#>  [47] IRanges_2.36.0              Matrix_1.6-0               
#>  [49] splines_4.3.1               igraph_1.5.0               
#>  [51] tidyselect_1.2.0            abind_1.4-5                
#>  [53] yaml_2.3.7                  hopach_2.62.0              
#>  [55] viridis_0.6.3               codetools_0.2-19           
#>  [57] minpack.lm_1.2-3            Cepo_1.8.0                 
#>  [59] lattice_0.21-8              tibble_3.2.1               
#>  [61] plyr_1.8.8                  Biobase_2.62.0             
#>  [63] withr_2.5.0                 evaluate_0.21              
#>  [65] survival_3.5-5              RcppParallel_5.1.7         
#>  [67] proxy_0.4-27                polyclip_1.10-4            
#>  [69] kernlab_0.9-32              pillar_1.9.0               
#>  [71] BiocManager_1.30.22         MatrixGenerics_1.14.0      
#>  [73] stats4_4.3.1                plotly_4.10.2              
#>  [75] generics_0.1.3              RCurl_1.98-1.12            
#>  [77] S4Vectors_0.40.1            ggplot2_3.4.2              
#>  [79] sparseMatrixStats_1.14.0    munsell_0.5.0              
#>  [81] scales_1.2.1                glue_1.6.2                 
#>  [83] lazyeval_0.2.2              proxyC_0.3.3               
#>  [85] tools_4.3.1                 data.table_1.14.8          
#>  [87] graphlayouts_1.0.0          tidygraph_1.2.3            
#>  [89] rhdf5_2.46.0                grid_4.3.1                 
#>  [91] tidyr_1.3.0                 colorspace_2.1-0           
#>  [93] SingleCellExperiment_1.24.0 nlme_3.1-162               
#>  [95] GenomeInfoDbData_1.2.10     patchwork_1.1.2            
#>  [97] ggforce_0.4.1               HDF5Array_1.30.0           
#>  [99] cli_3.6.1                   fansi_1.0.4                
#> [101] segmented_1.6-4             S4Arrays_1.2.0             
#> [103] viridisLite_0.4.2           dplyr_1.1.2                
#> [105] gtable_0.3.3                sass_0.4.6                 
#> [107] digest_0.6.33               BiocGenerics_0.48.0        
#> [109] SparseArray_1.2.0           ggrepel_0.9.3              
#> [111] htmlwidgets_1.6.2           farver_2.1.1               
#> [113] htmltools_0.5.5             lifecycle_1.0.3            
#> [115] httr_1.4.6                  statmod_1.5.0              
#> [117] MASS_7.3-60