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 4.4.1 (2024-06-14)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Ventura 13.6.7
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> 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_1.18.0 BiocStyle_2.34.0 
#> 
#> loaded via a namespace (and not attached):
#>   [1] gridExtra_2.3               rlang_1.1.4                
#>   [3] magrittr_2.0.3              matrixStats_1.4.1          
#>   [5] compiler_4.4.1              mgcv_1.9-1                 
#>   [7] DelayedMatrixStats_1.28.0   vctrs_0.6.5                
#>   [9] reshape2_1.4.4              stringr_1.5.1              
#>  [11] pkgconfig_2.0.3             crayon_1.5.3               
#>  [13] fastmap_1.2.0               magick_2.8.5               
#>  [15] XVector_0.46.0              labeling_0.4.3             
#>  [17] ggraph_2.2.1                utf8_1.2.4                 
#>  [19] rmarkdown_2.29              UCSC.utils_1.2.0           
#>  [21] tinytex_0.54                purrr_1.0.2                
#>  [23] xfun_0.49                   zlibbioc_1.52.0            
#>  [25] cachem_1.1.0                GenomeInfoDb_1.42.0        
#>  [27] jsonlite_1.8.9              rhdf5filters_1.18.0        
#>  [29] DelayedArray_0.32.0         Rhdf5lib_1.28.0            
#>  [31] BiocParallel_1.40.0         tweenr_2.0.3               
#>  [33] parallel_4.4.1              cluster_2.1.6              
#>  [35] R6_2.5.1                    bslib_0.8.0                
#>  [37] stringi_1.8.4               limma_3.62.1               
#>  [39] diptest_0.77-1              GenomicRanges_1.58.0       
#>  [41] jquerylib_0.1.4             Rcpp_1.0.13-1              
#>  [43] bookdown_0.41               SummarizedExperiment_1.36.0
#>  [45] knitr_1.49                  mixtools_2.0.0             
#>  [47] IRanges_2.40.0              Matrix_1.7-1               
#>  [49] splines_4.4.1               igraph_2.1.1               
#>  [51] tidyselect_1.2.1            abind_1.4-8                
#>  [53] yaml_2.3.10                 hopach_2.66.0              
#>  [55] viridis_0.6.5               codetools_0.2-20           
#>  [57] minpack.lm_1.2-4            Cepo_1.12.0                
#>  [59] lattice_0.22-6              tibble_3.2.1               
#>  [61] plyr_1.8.9                  Biobase_2.66.0             
#>  [63] withr_3.0.2                 evaluate_1.0.1             
#>  [65] survival_3.7-0              proxy_0.4-27               
#>  [67] polyclip_1.10-7             kernlab_0.9-33             
#>  [69] pillar_1.9.0                BiocManager_1.30.25        
#>  [71] MatrixGenerics_1.18.0       stats4_4.4.1               
#>  [73] plotly_4.10.4               generics_0.1.3             
#>  [75] S4Vectors_0.44.0            ggplot2_3.5.1              
#>  [77] sparseMatrixStats_1.18.0    munsell_0.5.1              
#>  [79] scales_1.3.0                glue_1.8.0                 
#>  [81] lazyeval_0.2.2              proxyC_0.4.1               
#>  [83] tools_4.4.1                 data.table_1.16.2          
#>  [85] graphlayouts_1.2.0          tidygraph_1.3.1            
#>  [87] rhdf5_2.50.0                grid_4.4.1                 
#>  [89] tidyr_1.3.1                 colorspace_2.1-1           
#>  [91] SingleCellExperiment_1.28.0 nlme_3.1-166               
#>  [93] GenomeInfoDbData_1.2.13     patchwork_1.3.0            
#>  [95] ggforce_0.4.2               HDF5Array_1.34.0           
#>  [97] cli_3.6.3                   fansi_1.0.6                
#>  [99] segmented_2.1-3             S4Arrays_1.6.0             
#> [101] viridisLite_0.4.2           dplyr_1.1.4                
#> [103] gtable_0.3.6                sass_0.4.9                 
#> [105] digest_0.6.37               BiocGenerics_0.52.0        
#> [107] SparseArray_1.6.0           ggrepel_0.9.6              
#> [109] htmlwidgets_1.6.4           farver_2.1.2               
#> [111] memoise_2.0.1               htmltools_0.5.8.1          
#> [113] lifecycle_1.0.4             httr_1.4.7                 
#> [115] statmod_1.5.0               MASS_7.3-61