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 4.3.0 RC (2023-04-13 r84269)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.17-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_1.12.0 BiocStyle_2.28.0 
#> 
#> loaded via a namespace (and not attached):
#>   [1] bitops_1.0-7                gridExtra_2.3              
#>   [3] rlang_1.1.0                 magrittr_2.0.3             
#>   [5] matrixStats_0.63.0          compiler_4.3.0             
#>   [7] mgcv_1.8-42                 DelayedMatrixStats_1.22.0  
#>   [9] vctrs_0.6.2                 reshape2_1.4.4             
#>  [11] stringr_1.5.0               pkgconfig_2.0.3            
#>  [13] fastmap_1.1.1               magick_2.7.4               
#>  [15] XVector_0.40.0              labeling_0.4.2             
#>  [17] ggraph_2.1.0                utf8_1.2.3                 
#>  [19] rmarkdown_2.21              purrr_1.0.1                
#>  [21] xfun_0.39                   zlibbioc_1.46.0            
#>  [23] cachem_1.0.7                GenomeInfoDb_1.36.0        
#>  [25] jsonlite_1.8.4              highr_0.10                 
#>  [27] rhdf5filters_1.12.0         DelayedArray_0.26.0        
#>  [29] Rhdf5lib_1.22.0             BiocParallel_1.34.0        
#>  [31] tweenr_2.0.2                parallel_4.3.0             
#>  [33] cluster_2.1.4               R6_2.5.1                   
#>  [35] bslib_0.4.2                 stringi_1.7.12             
#>  [37] limma_3.56.0                diptest_0.76-0             
#>  [39] GenomicRanges_1.52.0        jquerylib_0.1.4            
#>  [41] Rcpp_1.0.10                 bookdown_0.33              
#>  [43] SummarizedExperiment_1.30.0 knitr_1.42                 
#>  [45] mixtools_2.0.0              IRanges_2.34.0             
#>  [47] Matrix_1.5-4                splines_4.3.0              
#>  [49] igraph_1.4.2                tidyselect_1.2.0           
#>  [51] yaml_2.3.7                  hopach_2.60.0              
#>  [53] viridis_0.6.2               codetools_0.2-19           
#>  [55] minpack.lm_1.2-3            Cepo_1.6.0                 
#>  [57] lattice_0.21-8              tibble_3.2.1               
#>  [59] plyr_1.8.8                  Biobase_2.60.0             
#>  [61] withr_2.5.0                 evaluate_0.20              
#>  [63] survival_3.5-5              RcppParallel_5.1.7         
#>  [65] proxy_0.4-27                polyclip_1.10-4            
#>  [67] kernlab_0.9-32              pillar_1.9.0               
#>  [69] BiocManager_1.30.20         MatrixGenerics_1.12.0      
#>  [71] stats4_4.3.0                plotly_4.10.1              
#>  [73] generics_0.1.3              RCurl_1.98-1.12            
#>  [75] S4Vectors_0.38.0            ggplot2_3.4.2              
#>  [77] sparseMatrixStats_1.12.0    munsell_0.5.0              
#>  [79] scales_1.2.1                glue_1.6.2                 
#>  [81] lazyeval_0.2.2              proxyC_0.3.3               
#>  [83] tools_4.3.0                 data.table_1.14.8          
#>  [85] graphlayouts_0.8.4          tidygraph_1.2.3            
#>  [87] rhdf5_2.44.0                grid_4.3.0                 
#>  [89] tidyr_1.3.0                 colorspace_2.1-0           
#>  [91] SingleCellExperiment_1.22.0 nlme_3.1-162               
#>  [93] GenomeInfoDbData_1.2.10     patchwork_1.1.2            
#>  [95] ggforce_0.4.1               HDF5Array_1.28.0           
#>  [97] cli_3.6.1                   fansi_1.0.4                
#>  [99] segmented_1.6-4             viridisLite_0.4.1          
#> [101] dplyr_1.1.2                 gtable_0.3.3               
#> [103] sass_0.4.5                  digest_0.6.31              
#> [105] BiocGenerics_0.46.0         ggrepel_0.9.3              
#> [107] htmlwidgets_1.6.2           farver_2.1.1               
#> [109] htmltools_0.5.5             lifecycle_1.0.3            
#> [111] httr_1.4.5                  statmod_1.5.0              
#> [113] MASS_7.3-59