# Performing scClassify using pretrained model

Yingxin Lin1

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

#### 26 April 2022

# 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 (2022-04-19 r82224 ucrt)
    #> Platform: x86_64-w64-mingw32/x64 (64-bit)
    #> Running under: Windows Server 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
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] scClassify_1.8.0 BiocStyle_2.24.0
    #>
    #> 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_2.56.0               GenomeInfoDb_1.32.0
    #>   [7] tools_4.2.0                 bslib_0.3.1
    #>   [9] utf8_1.2.2                  R6_2.5.1
    #>  [11] HDF5Array_1.24.0            mgcv_1.8-40
    #>  [13] DBI_1.1.2                   BiocGenerics_0.42.0
    #>  [15] colorspace_2.0-3            rhdf5filters_1.8.0
    #>  [17] gridExtra_2.3               tidyselect_1.1.2
    #>  [19] proxyC_0.2.4                compiler_4.2.0
    #>  [21] cli_3.3.0                   Biobase_2.56.0
    #>  [23] DelayedArray_0.22.0         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_0.36.0
    #>  [35] pkgconfig_2.0.3             htmltools_0.5.2
    #>  [37] sparseMatrixStats_1.8.0     Cepo_1.2.0
    #>  [39] MatrixGenerics_1.8.0        highr_0.9
    #>  [41] fastmap_1.1.0               limma_3.52.0
    #>  [43] rlang_1.0.2                 DelayedMatrixStats_1.18.0
    #>  [45] jquerylib_0.1.4             generics_0.1.2
    #>  [47] farver_2.1.0                jsonlite_1.8.0
    #>  [49] BiocParallel_1.30.0         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_0.34.0
    #>  [59] Rhdf5lib_1.18.0             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_1.26.0 zlibbioc_1.42.0
    #>  [69] rhdf5_2.40.0                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_1.48.0
    #>  [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_1.18.0
    #> [105] tibble_3.1.6                IRanges_2.30.0
    #> [107] cluster_2.1.3               statmod_1.4.36
    #> [109] ellipsis_0.3.2