# Performing scClassify using pretrained model

Yingxin Lin1

1School of Mathematics and Statistics, The University of Sydney, Australia

#### 15 April 2025

# 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 4.5.0 RC (2025-04-04 r88126)
    #> Platform: x86_64-apple-darwin20
    #> Running under: macOS Monterey 12.7.6
    #>
    #> Matrix products: default
    #> BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    #>
    #> 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_1.20.0 BiocStyle_2.36.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] gridExtra_2.3               rlang_1.1.6
    #>   [3] magrittr_2.0.3              matrixStats_1.5.0
    #>   [5] compiler_4.5.0              mgcv_1.9-3
    #>   [7] DelayedMatrixStats_1.30.0   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_2.8.6
    #>  [15] XVector_0.48.0              labeling_0.4.3
    #>  [17] ggraph_2.2.1                rmarkdown_2.29
    #>  [19] UCSC.utils_1.4.0            tinytex_0.57
    #>  [21] purrr_1.0.4                 xfun_0.52
    #>  [23] cachem_1.1.0                GenomeInfoDb_1.44.0
    #>  [25] jsonlite_2.0.0              rhdf5filters_1.20.0
    #>  [27] DelayedArray_0.34.0         Rhdf5lib_1.30.0
    #>  [29] BiocParallel_1.42.0         tweenr_2.0.3
    #>  [31] parallel_4.5.0              cluster_2.1.8.1
    #>  [33] R6_2.6.1                    bslib_0.9.0
    #>  [35] stringi_1.8.7               limma_3.64.0
    #>  [37] diptest_0.77-1              GenomicRanges_1.60.0
    #>  [39] jquerylib_0.1.4             Rcpp_1.0.14
    #>  [41] bookdown_0.43               SummarizedExperiment_1.38.0
    #>  [43] knitr_1.50                  mixtools_2.0.0.1
    #>  [45] IRanges_2.42.0              Matrix_1.7-3
    #>  [47] splines_4.5.0               igraph_2.1.4
    #>  [49] tidyselect_1.2.1            abind_1.4-8
    #>  [51] yaml_2.3.10                 hopach_2.68.0
    #>  [53] viridis_0.6.5               codetools_0.2-20
    #>  [55] minpack.lm_1.2-4            Cepo_1.14.0
    #>  [57] lattice_0.22-7              tibble_3.2.1
    #>  [59] plyr_1.8.9                  Biobase_2.68.0
    #>  [61] withr_3.0.2                 evaluate_1.0.3
    #>  [63] survival_3.8-3              proxy_0.4-27
    #>  [65] polyclip_1.10-7             kernlab_0.9-33
    #>  [67] pillar_1.10.2               BiocManager_1.30.25
    #>  [69] MatrixGenerics_1.20.0       stats4_4.5.0
    #>  [71] plotly_4.10.4               generics_0.1.3
    #>  [73] S4Vectors_0.46.0            ggplot2_3.5.2
    #>  [75] sparseMatrixStats_1.20.0    munsell_0.5.1
    #>  [77] scales_1.3.0                glue_1.8.0
    #>  [79] lazyeval_0.2.2              proxyC_0.4.1
    #>  [81] tools_4.5.0                 data.table_1.17.0
    #>  [83] graphlayouts_1.2.2          tidygraph_1.3.1
    #>  [85] rhdf5_2.52.0                grid_4.5.0
    #>  [87] tidyr_1.3.1                 colorspace_2.1-1
    #>  [89] SingleCellExperiment_1.30.0 nlme_3.1-168
    #>  [91] GenomeInfoDbData_1.2.14     patchwork_1.3.0
    #>  [93] ggforce_0.4.2               HDF5Array_1.36.0
    #>  [95] cli_3.6.4                   segmented_2.1-4
    #>  [97] S4Arrays_1.8.0              viridisLite_0.4.2
    #>  [99] dplyr_1.1.4                 gtable_0.3.6
    #> [101] sass_0.4.10                 digest_0.6.37
    #> [103] BiocGenerics_0.54.0         SparseArray_1.8.0
    #> [105] ggrepel_0.9.6               htmlwidgets_1.6.4
    #> [107] farver_2.1.2                memoise_2.0.1
    #> [109] htmltools_0.5.8.1           lifecycle_1.0.4
    #> [111] h5mread_1.0.0               httr_1.4.7
    #> [113] statmod_1.5.0               MASS_7.3-65