# Performing scClassify using pretrained model

Yingxin Lin1

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

#### 27 October 2020

# 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:
    #> numeric(0)

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 4.0.3 (2020-10-10)
    #> Platform: x86_64-pc-linux-gnu (64-bit)
    #> Running under: Ubuntu 18.04.5 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
    #> LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        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_1.2.0 BiocStyle_2.18.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] Biobase_2.50.0      viridis_0.5.1       mixtools_1.2.0
    #>  [4] tidyr_1.1.2         tidygraph_1.2.0     viridisLite_0.3.0
    #>  [7] splines_4.0.3       ggraph_2.0.3        RcppParallel_5.0.2
    #> [10] statmod_1.4.35      BiocManager_1.30.10 stats4_4.0.3
    #> [13] yaml_2.2.1          ggrepel_0.8.2       pillar_1.4.6
    #> [16] lattice_0.20-41     glue_1.4.2          limma_3.46.0
    #> [19] digest_0.6.27       polyclip_1.10-0     colorspace_1.4-1
    #> [22] htmltools_0.5.0     Matrix_1.2-18       pkgconfig_2.0.3
    #> [25] magick_2.5.0        bookdown_0.21       purrr_0.3.4
    #> [28] scales_1.1.1        tweenr_1.0.1        hopach_2.50.0
    #> [31] BiocParallel_1.24.0 ggforce_0.3.2       tibble_3.0.4
    #> [34] proxy_0.4-24        mgcv_1.8-33         generics_0.0.2
    #> [37] farver_2.0.3        ggplot2_3.3.2       ellipsis_0.3.1
    #> [40] BiocGenerics_0.36.0 survival_3.2-7      magrittr_1.5
    #> [43] crayon_1.3.4        evaluate_0.14       nlme_3.1-150
    #> [46] MASS_7.3-53         segmented_1.3-0     tools_4.0.3
    #> [49] minpack.lm_1.2-1    lifecycle_0.2.0     stringr_1.4.0
    #> [52] S4Vectors_0.28.0    kernlab_0.9-29      munsell_0.5.0
    #> [55] cluster_2.1.0       compiler_4.0.3      proxyC_0.1.5
    #> [58] rlang_0.4.8         grid_4.0.3          igraph_1.2.6
    #> [61] labeling_0.4.2      rmarkdown_2.5       gtable_0.3.0
    #> [64] graphlayouts_0.7.1  R6_2.4.1            gridExtra_2.3
    #> [67] knitr_1.30          dplyr_1.0.2         stringi_1.5.3
    #> [70] parallel_4.0.3      Rcpp_1.0.5          vctrs_0.3.4
    #> [73] diptest_0.75-7      tidyselect_1.1.0    xfun_0.18