Performing scClassify using pretrained model
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.
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: 4In 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:
We can also visualise the cell type tree of the reference data.
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 NANoted 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 45Prediction results for spearman as the similarity metric:
Session Info
sessionInfo()
#> R version 4.5.1 (2025-06-13)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> 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
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] scClassify_1.23.0 BiocStyle_2.37.1
#>
#> loaded via a namespace (and not attached):
#> [1] gridExtra_2.3 rlang_1.1.6
#> [3] magrittr_2.0.4 matrixStats_1.5.0
#> [5] compiler_4.5.1 mgcv_1.9-3
#> [7] DelayedMatrixStats_1.31.0 vctrs_0.6.5
#> [9] reshape2_1.4.4 stringr_1.5.2
#> [11] pkgconfig_2.0.3 fastmap_1.2.0
#> [13] XVector_0.49.3 labeling_0.4.3
#> [15] ggraph_2.2.2 rmarkdown_2.30
#> [17] purrr_1.1.0 xfun_0.53
#> [19] cachem_1.1.0 jsonlite_2.0.0
#> [21] rhdf5filters_1.23.0 DelayedArray_0.37.0
#> [23] Rhdf5lib_1.33.0 BiocParallel_1.43.4
#> [25] tweenr_2.0.3 parallel_4.5.1
#> [27] cluster_2.1.8.1 R6_2.6.1
#> [29] bslib_0.9.0 stringi_1.8.7
#> [31] RColorBrewer_1.1-3 limma_3.65.7
#> [33] diptest_0.77-2 GenomicRanges_1.63.0
#> [35] jquerylib_0.1.4 Rcpp_1.1.0
#> [37] Seqinfo_0.99.4 SummarizedExperiment_1.39.2
#> [39] knitr_1.50 mixtools_2.0.0.1
#> [41] IRanges_2.45.0 Matrix_1.7-4
#> [43] splines_4.5.1 igraph_2.2.1
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#> [47] yaml_2.3.10 hopach_2.71.0
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#> [51] minpack.lm_1.2-4 Cepo_1.17.0
#> [53] lattice_0.22-7 tibble_3.3.0
#> [55] plyr_1.8.9 Biobase_2.69.1
#> [57] withr_3.0.2 S7_0.2.0
#> [59] evaluate_1.0.5 survival_3.8-3
#> [61] proxy_0.4-27 polyclip_1.10-7
#> [63] kernlab_0.9-33 pillar_1.11.1
#> [65] BiocManager_1.30.26 MatrixGenerics_1.21.0
#> [67] stats4_4.5.1 plotly_4.11.0
#> [69] generics_0.1.4 S4Vectors_0.47.6
#> [71] ggplot2_4.0.0 sparseMatrixStats_1.21.0
#> [73] scales_1.4.0 glue_1.8.0
#> [75] lazyeval_0.2.2 proxyC_0.5.2
#> [77] maketools_1.3.2 tools_4.5.1
#> [79] sys_3.4.3 data.table_1.17.8
#> [81] buildtools_1.0.0 graphlayouts_1.2.2
#> [83] tidygraph_1.3.1 rhdf5_2.55.0
#> [85] grid_4.5.1 tidyr_1.3.1
#> [87] SingleCellExperiment_1.31.1 nlme_3.1-168
#> [89] patchwork_1.3.2 ggforce_0.5.0
#> [91] HDF5Array_1.37.0 cli_3.6.5
#> [93] segmented_2.1-4 S4Arrays_1.9.3
#> [95] viridisLite_0.4.2 dplyr_1.1.4
#> [97] gtable_0.3.6 sass_0.4.10
#> [99] digest_0.6.37 BiocGenerics_0.55.4
#> [101] SparseArray_1.9.2 ggrepel_0.9.6
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#> [107] lifecycle_1.0.4 h5mread_1.1.1
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#> [111] MASS_7.3-65