# Performing scClassify using pretrained model

Yingxin Lin1

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

#### 26 October 2021

# 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 4.1.1 Patched (2021-08-22 r80813)
    #> Platform: x86_64-apple-darwin17.0 (64-bit)
    #> Running under: macOS Mojave 10.14.6
    #>
    #> Matrix products: default
    #> BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
    #>
    #> locale:
    #> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] scClassify_1.6.0 BiocStyle_2.22.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] segmented_1.3-4             nlme_3.1-153
    #>   [3] bitops_1.0-7                matrixStats_0.61.0
    #>   [5] hopach_2.54.0               GenomeInfoDb_1.30.0
    #>   [7] tools_4.1.1                 bslib_0.3.1
    #>   [9] utf8_1.2.2                  R6_2.5.1
    #>  [11] HDF5Array_1.22.0            mgcv_1.8-38
    #>  [13] DBI_1.1.1                   BiocGenerics_0.40.0
    #>  [15] colorspace_2.0-2            rhdf5filters_1.6.0
    #>  [17] gridExtra_2.3               tidyselect_1.1.1
    #>  [19] proxyC_0.2.1                compiler_4.1.1
    #>  [21] Biobase_2.54.0              DelayedArray_0.20.0
    #>  [23] labeling_0.4.2              bookdown_0.24
    #>  [25] sass_0.4.0                  diptest_0.76-0
    #>  [27] scales_1.1.1                proxy_0.4-26
    #>  [29] stringr_1.4.0               digest_0.6.28
    #>  [31] mixtools_1.2.0              rmarkdown_2.11
    #>  [33] XVector_0.34.0              pkgconfig_2.0.3
    #>  [35] htmltools_0.5.2             sparseMatrixStats_1.6.0
    #>  [37] Cepo_1.0.0                  MatrixGenerics_1.6.0
    #>  [39] highr_0.9                   fastmap_1.1.0
    #>  [41] limma_3.50.0                rlang_0.4.12
    #>  [43] DelayedMatrixStats_1.16.0   jquerylib_0.1.4
    #>  [45] generics_0.1.1              farver_2.1.0
    #>  [47] jsonlite_1.7.2              BiocParallel_1.28.0
    #>  [49] dplyr_1.0.7                 RCurl_1.98-1.5
    #>  [51] magrittr_2.0.1              GenomeInfoDbData_1.2.7
    #>  [53] patchwork_1.1.1             Matrix_1.3-4
    #>  [55] Rcpp_1.0.7                  munsell_0.5.0
    #>  [57] S4Vectors_0.32.0            Rhdf5lib_1.16.0
    #>  [59] fansi_0.5.0                 viridis_0.6.2
    #>  [61] lifecycle_1.0.1             stringi_1.7.5
    #>  [63] yaml_2.2.1                  ggraph_2.0.5
    #>  [65] MASS_7.3-54                 SummarizedExperiment_1.24.0
    #>  [67] zlibbioc_1.40.0             rhdf5_2.38.0
    #>  [69] plyr_1.8.6                  grid_4.1.1
    #>  [71] parallel_4.1.1              ggrepel_0.9.1
    #>  [73] crayon_1.4.1                lattice_0.20-45
    #>  [75] splines_4.1.1               graphlayouts_0.7.1
    #>  [77] magick_2.7.3                knitr_1.36
    #>  [79] pillar_1.6.4                igraph_1.2.7
    #>  [81] GenomicRanges_1.46.0        reshape2_1.4.4
    #>  [83] stats4_4.1.1                glue_1.4.2
    #>  [85] evaluate_0.14               RcppParallel_5.1.4
    #>  [87] BiocManager_1.30.16         vctrs_0.3.8
    #>  [89] tweenr_1.0.2                gtable_0.3.0
    #>  [91] purrr_0.3.4                 polyclip_1.10-0
    #>  [93] tidyr_1.1.4                 kernlab_0.9-29
    #>  [95] assertthat_0.2.1            ggplot2_3.3.5
    #>  [97] xfun_0.27                   ggforce_0.3.3
    #>  [99] tidygraph_1.2.0             survival_3.2-13
    #> [101] viridisLite_0.4.0           minpack.lm_1.2-1
    #> [103] SingleCellExperiment_1.16.0 tibble_3.1.5
    #> [105] IRanges_2.28.0              cluster_2.1.2
    #> [107] statmod_1.4.36              ellipsis_0.3.2