# Performing scClassify using pretrained model

Yingxin Lin1

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

#### 19 May 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 4.1.0 RC (2021-05-10 r80283)
    #> Platform: x86_64-w64-mingw32/x64 (64-bit)
    #> Running under: Windows Server x64 (build 17763)
    #>
    #> Matrix products: default
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.1252
    #> [3] LC_MONETARY=English_United States.1252
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.1252
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] scClassify_1.4.0 BiocStyle_2.20.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] Biobase_2.52.0      viridis_0.6.1       mixtools_1.2.0
    #>  [4] sass_0.4.0          tidyr_1.1.3         tidygraph_1.2.0
    #>  [7] jsonlite_1.7.2      viridisLite_0.4.0   splines_4.1.0
    #> [10] ggraph_2.0.5        RcppParallel_5.1.4  bslib_0.2.5.1
    #> [13] assertthat_0.2.1    statmod_1.4.36      highr_0.9
    #> [16] BiocManager_1.30.15 stats4_4.1.0        yaml_2.2.1
    #> [19] ggrepel_0.9.1       pillar_1.6.1        lattice_0.20-44
    #> [22] glue_1.4.2          limma_3.48.0        digest_0.6.27
    #> [25] polyclip_1.10-0     colorspace_2.0-1    htmltools_0.5.1.1
    #> [28] Matrix_1.3-3        pkgconfig_2.0.3     magick_2.7.2
    #> [31] bookdown_0.22       purrr_0.3.4         scales_1.1.1
    #> [34] tweenr_1.0.2        hopach_2.52.0       BiocParallel_1.26.0
    #> [37] ggforce_0.3.3       proxy_0.4-25        tibble_3.1.2
    #> [40] mgcv_1.8-35         generics_0.1.0      farver_2.1.0
    #> [43] ggplot2_3.3.3       ellipsis_0.3.2      BiocGenerics_0.38.0
    #> [46] survival_3.2-11     magrittr_2.0.1      crayon_1.4.1
    #> [49] evaluate_0.14       fansi_0.4.2         nlme_3.1-152
    #> [52] MASS_7.3-54         segmented_1.3-4     tools_4.1.0
    #> [55] minpack.lm_1.2-1    lifecycle_1.0.0     stringr_1.4.0
    #> [58] S4Vectors_0.30.0    kernlab_0.9-29      munsell_0.5.0
    #> [61] cluster_2.1.2       compiler_4.1.0      jquerylib_0.1.4
    #> [64] proxyC_0.2.0        rlang_0.4.11        grid_4.1.0
    #> [67] igraph_1.2.6        labeling_0.4.2      rmarkdown_2.8
    #> [70] gtable_0.3.0        DBI_1.1.1           graphlayouts_0.7.1
    #> [73] R6_2.5.0            gridExtra_2.3       knitr_1.33
    #> [76] dplyr_1.0.6         utf8_1.2.1          stringi_1.6.2
    #> [79] parallel_4.1.0      Rcpp_1.0.6          vctrs_0.3.8
    #> [82] diptest_0.76-0      tidyselect_1.1.1    xfun_0.23