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: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.18-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_GB              LC_COLLATE=C              
#>  [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] 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_0.6.4                 reshape2_1.4.4             
#>  [11] stringr_1.5.0               pkgconfig_2.0.3            
#>  [13] crayon_1.5.2                fastmap_1.1.1              
#>  [15] magick_2.8.1                XVector_0.42.0             
#>  [17] labeling_0.4.3              ggraph_2.1.0               
#>  [19] utf8_1.2.4                  rmarkdown_2.25             
#>  [21] purrr_1.0.2                 xfun_0.40                  
#>  [23] zlibbioc_1.48.0             cachem_1.0.8               
#>  [25] GenomeInfoDb_1.38.0         jsonlite_1.8.7             
#>  [27] rhdf5filters_1.14.0         DelayedArray_0.28.0        
#>  [29] Rhdf5lib_1.24.0             BiocParallel_1.36.0        
#>  [31] tweenr_2.0.2                parallel_4.3.1             
#>  [33] cluster_2.1.4               R6_2.5.1                   
#>  [35] bslib_0.5.1                 stringi_1.7.12             
#>  [37] limma_3.58.0                diptest_0.76-0             
#>  [39] GenomicRanges_1.54.0        jquerylib_0.1.4            
#>  [41] Rcpp_1.0.11                 bookdown_0.36              
#>  [43] SummarizedExperiment_1.32.0 knitr_1.44                 
#>  [45] mixtools_2.0.0              IRanges_2.36.0             
#>  [47] Matrix_1.6-1.1              splines_4.3.1              
#>  [49] igraph_1.5.1                tidyselect_1.2.0           
#>  [51] abind_1.4-5                 yaml_2.3.7                 
#>  [53] hopach_2.62.0               viridis_0.6.4              
#>  [55] codetools_0.2-19            minpack.lm_1.2-4           
#>  [57] Cepo_1.8.0                  lattice_0.22-5             
#>  [59] tibble_3.2.1                plyr_1.8.9                 
#>  [61] Biobase_2.62.0              withr_2.5.1                
#>  [63] evaluate_0.22               survival_3.5-7             
#>  [65] RcppParallel_5.1.7          proxy_0.4-27               
#>  [67] polyclip_1.10-6             kernlab_0.9-32             
#>  [69] pillar_1.9.0                BiocManager_1.30.22        
#>  [71] MatrixGenerics_1.14.0       stats4_4.3.1               
#>  [73] plotly_4.10.3               generics_0.1.3             
#>  [75] RCurl_1.98-1.12             S4Vectors_0.40.0           
#>  [77] ggplot2_3.4.4               sparseMatrixStats_1.14.0   
#>  [79] munsell_0.5.0               scales_1.2.1               
#>  [81] glue_1.6.2                  lazyeval_0.2.2             
#>  [83] proxyC_0.3.3                tools_4.3.1                
#>  [85] data.table_1.14.8           graphlayouts_1.0.1         
#>  [87] tidygraph_1.2.3             rhdf5_2.46.0               
#>  [89] grid_4.3.1                  tidyr_1.3.0                
#>  [91] colorspace_2.1-0            SingleCellExperiment_1.24.0
#>  [93] nlme_3.1-163                GenomeInfoDbData_1.2.11    
#>  [95] patchwork_1.1.3             ggforce_0.4.1              
#>  [97] HDF5Array_1.30.0            cli_3.6.1                  
#>  [99] fansi_1.0.5                 segmented_1.6-4            
#> [101] S4Arrays_1.2.0              viridisLite_0.4.2          
#> [103] dplyr_1.1.3                 gtable_0.3.4               
#> [105] sass_0.4.7                  digest_0.6.33              
#> [107] BiocGenerics_0.48.0         SparseArray_1.2.0          
#> [109] ggrepel_0.9.4               htmlwidgets_1.6.2          
#> [111] farver_2.1.1                htmltools_0.5.6.1          
#> [113] lifecycle_1.0.3             httr_1.4.7                 
#> [115] statmod_1.5.0               MASS_7.3-60