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 4.2.0 RC (2022-04-21 r82226)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
#> 
#> 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       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] scClassify_1.9.0 BiocStyle_2.25.0
#> 
#> loaded via a namespace (and not attached):
#>   [1] segmented_1.5-0             nlme_3.1-157               
#>   [3] bitops_1.0-7                matrixStats_0.62.0         
#>   [5] hopach_2.57.0               GenomeInfoDb_1.33.0        
#>   [7] tools_4.2.0                 bslib_0.3.1                
#>   [9] utf8_1.2.2                  R6_2.5.1                   
#>  [11] HDF5Array_1.25.0            mgcv_1.8-40                
#>  [13] DBI_1.1.2                   BiocGenerics_0.43.0        
#>  [15] colorspace_2.0-3            rhdf5filters_1.9.0         
#>  [17] gridExtra_2.3               tidyselect_1.1.2           
#>  [19] proxyC_0.2.4                compiler_4.2.0             
#>  [21] cli_3.3.0                   Biobase_2.57.0             
#>  [23] DelayedArray_0.23.0         labeling_0.4.2             
#>  [25] bookdown_0.26               sass_0.4.1                 
#>  [27] diptest_0.76-0              scales_1.2.0               
#>  [29] proxy_0.4-26                stringr_1.4.0              
#>  [31] digest_0.6.29               mixtools_1.2.0             
#>  [33] rmarkdown_2.14              XVector_0.37.0             
#>  [35] pkgconfig_2.0.3             htmltools_0.5.2            
#>  [37] sparseMatrixStats_1.9.0     Cepo_1.3.0                 
#>  [39] MatrixGenerics_1.9.0        highr_0.9                  
#>  [41] fastmap_1.1.0               limma_3.53.0               
#>  [43] rlang_1.0.2                 DelayedMatrixStats_1.19.0  
#>  [45] jquerylib_0.1.4             generics_0.1.2             
#>  [47] farver_2.1.0                jsonlite_1.8.0             
#>  [49] BiocParallel_1.31.0         dplyr_1.0.8                
#>  [51] RCurl_1.98-1.6              magrittr_2.0.3             
#>  [53] GenomeInfoDbData_1.2.8      patchwork_1.1.1            
#>  [55] Matrix_1.4-1                Rcpp_1.0.8.3               
#>  [57] munsell_0.5.0               S4Vectors_0.35.0           
#>  [59] Rhdf5lib_1.19.0             fansi_1.0.3                
#>  [61] viridis_0.6.2               lifecycle_1.0.1            
#>  [63] stringi_1.7.6               yaml_2.3.5                 
#>  [65] ggraph_2.0.5                MASS_7.3-57                
#>  [67] SummarizedExperiment_1.27.0 zlibbioc_1.43.0            
#>  [69] rhdf5_2.41.0                plyr_1.8.7                 
#>  [71] grid_4.2.0                  parallel_4.2.0             
#>  [73] ggrepel_0.9.1               crayon_1.5.1               
#>  [75] lattice_0.20-45             splines_4.2.0              
#>  [77] graphlayouts_0.8.0          magick_2.7.3               
#>  [79] knitr_1.38                  pillar_1.7.0               
#>  [81] igraph_1.3.1                GenomicRanges_1.49.0       
#>  [83] reshape2_1.4.4              stats4_4.2.0               
#>  [85] glue_1.6.2                  evaluate_0.15              
#>  [87] RcppParallel_5.1.5          BiocManager_1.30.17        
#>  [89] vctrs_0.4.1                 tweenr_1.0.2               
#>  [91] gtable_0.3.0                purrr_0.3.4                
#>  [93] polyclip_1.10-0             tidyr_1.2.0                
#>  [95] kernlab_0.9-30              assertthat_0.2.1           
#>  [97] ggplot2_3.3.5               xfun_0.30                  
#>  [99] ggforce_0.3.3               tidygraph_1.2.1            
#> [101] survival_3.3-1              viridisLite_0.4.0          
#> [103] minpack.lm_1.2-2            SingleCellExperiment_1.19.0
#> [105] tibble_3.1.6                IRanges_2.31.0             
#> [107] cluster_2.1.3               statmod_1.4.36             
#> [109] ellipsis_0.3.2