# Performing scClassify using pretrained model

Yingxin Lin1

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

#### 3 July 2024

# 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 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows Server 2022 x64 (build 20348)
    #>
    #> Matrix products: default
    #>
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] scClassify_1.16.0 BiocStyle_2.32.1
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] gridExtra_2.3               rlang_1.1.4
    #>   [3] magrittr_2.0.3              matrixStats_1.3.0
    #>   [5] compiler_4.4.1              mgcv_1.9-1
    #>   [7] DelayedMatrixStats_1.26.0   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_2.8.3
    #>  [15] XVector_0.44.0              labeling_0.4.3
    #>  [17] ggraph_2.2.1                utf8_1.2.4
    #>  [19] rmarkdown_2.27              UCSC.utils_1.0.0
    #>  [21] tinytex_0.51                purrr_1.0.2
    #>  [23] xfun_0.45                   zlibbioc_1.50.0
    #>  [25] cachem_1.1.0                GenomeInfoDb_1.40.1
    #>  [27] jsonlite_1.8.8              highr_0.11
    #>  [29] rhdf5filters_1.16.0         DelayedArray_0.30.1
    #>  [31] Rhdf5lib_1.26.0             BiocParallel_1.38.0
    #>  [33] tweenr_2.0.3                parallel_4.4.1
    #>  [35] cluster_2.1.6               R6_2.5.1
    #>  [37] bslib_0.7.0                 stringi_1.8.4
    #>  [39] limma_3.60.3                diptest_0.77-1
    #>  [41] GenomicRanges_1.56.1        jquerylib_0.1.4
    #>  [43] Rcpp_1.0.12                 bookdown_0.40
    #>  [45] SummarizedExperiment_1.34.0 knitr_1.47
    #>  [47] mixtools_2.0.0              IRanges_2.38.1
    #>  [49] Matrix_1.7-0                splines_4.4.1
    #>  [51] igraph_2.0.3                tidyselect_1.2.1
    #>  [53] abind_1.4-5                 yaml_2.3.8
    #>  [55] hopach_2.64.0               viridis_0.6.5
    #>  [57] codetools_0.2-20            minpack.lm_1.2-4
    #>  [59] Cepo_1.10.2                 lattice_0.22-6
    #>  [61] tibble_3.2.1                plyr_1.8.9
    #>  [63] Biobase_2.64.0              withr_3.0.0
    #>  [65] evaluate_0.24.0             survival_3.7-0
    #>  [67] proxy_0.4-27                polyclip_1.10-6
    #>  [69] kernlab_0.9-32              pillar_1.9.0
    #>  [71] BiocManager_1.30.23         MatrixGenerics_1.16.0
    #>  [73] stats4_4.4.1                plotly_4.10.4
    #>  [75] generics_0.1.3              S4Vectors_0.42.1
    #>  [77] ggplot2_3.5.1               sparseMatrixStats_1.16.0
    #>  [79] munsell_0.5.1               scales_1.3.0
    #>  [81] glue_1.7.0                  lazyeval_0.2.2
    #>  [83] proxyC_0.4.1                tools_4.4.1
    #>  [85] data.table_1.15.4           graphlayouts_1.1.1
    #>  [87] tidygraph_1.3.1             rhdf5_2.48.0
    #>  [89] grid_4.4.1                  tidyr_1.3.1
    #>  [91] colorspace_2.1-0            SingleCellExperiment_1.26.0
    #>  [93] nlme_3.1-165                GenomeInfoDbData_1.2.12
    #>  [95] patchwork_1.2.0             ggforce_0.4.2
    #>  [97] HDF5Array_1.32.0            cli_3.6.3
    #>  [99] fansi_1.0.6                 segmented_2.1-0
    #> [101] S4Arrays_1.4.1              viridisLite_0.4.2
    #> [103] dplyr_1.1.4                 gtable_0.3.5
    #> [105] sass_0.4.9                  digest_0.6.36
    #> [107] BiocGenerics_0.50.0         SparseArray_1.4.8
    #> [109] ggrepel_0.9.5               htmlwidgets_1.6.4
    #> [111] farver_2.1.2                memoise_2.0.1
    #> [113] htmltools_0.5.8.1           lifecycle_1.0.4
    #> [115] httr_1.4.7                  statmod_1.5.0
    #> [117] MASS_7.3-61