Performing scClassify using pretrained model
3 July 2024
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: 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:
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 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:
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 44 Session Info
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] scClassify_1.16.0 BiocStyle_2.32.1
#>
#> loaded via a namespace (and not attached):
#> [1] gridExtra_2.3 rlang_1.1.4
#> [3] magrittr_2.0.3 matrixStats_1.3.0
#> [5] compiler_4.4.1 mgcv_1.9-1
#> [7] DelayedMatrixStats_1.26.0 vctrs_0.6.5
#> [9] reshape2_1.4.4 stringr_1.5.1
#> [11] pkgconfig_2.0.3 crayon_1.5.3
#> [13] fastmap_1.2.0 magick_2.8.3
#> [15] XVector_0.44.0 labeling_0.4.3
#> [17] ggraph_2.2.1 utf8_1.2.4
#> [19] rmarkdown_2.27 UCSC.utils_1.0.0
#> [21] tinytex_0.51 purrr_1.0.2
#> [23] xfun_0.45 zlibbioc_1.50.0
#> [25] cachem_1.1.0 GenomeInfoDb_1.40.1
#> [27] jsonlite_1.8.8 highr_0.11
#> [29] rhdf5filters_1.16.0 DelayedArray_0.30.1
#> [31] Rhdf5lib_1.26.0 BiocParallel_1.38.0
#> [33] tweenr_2.0.3 parallel_4.4.1
#> [35] cluster_2.1.6 R6_2.5.1
#> [37] bslib_0.7.0 stringi_1.8.4
#> [39] limma_3.60.3 diptest_0.77-1
#> [41] GenomicRanges_1.56.1 jquerylib_0.1.4
#> [43] Rcpp_1.0.12 bookdown_0.40
#> [45] SummarizedExperiment_1.34.0 knitr_1.47
#> [47] mixtools_2.0.0 IRanges_2.38.1
#> [49] Matrix_1.7-0 splines_4.4.1
#> [51] igraph_2.0.3 tidyselect_1.2.1
#> [53] abind_1.4-5 yaml_2.3.8
#> [55] hopach_2.64.0 viridis_0.6.5
#> [57] codetools_0.2-20 minpack.lm_1.2-4
#> [59] Cepo_1.10.2 lattice_0.22-6
#> [61] tibble_3.2.1 plyr_1.8.9
#> [63] Biobase_2.64.0 withr_3.0.0
#> [65] evaluate_0.24.0 survival_3.7-0
#> [67] proxy_0.4-27 polyclip_1.10-6
#> [69] kernlab_0.9-32 pillar_1.9.0
#> [71] BiocManager_1.30.23 MatrixGenerics_1.16.0
#> [73] stats4_4.4.1 plotly_4.10.4
#> [75] generics_0.1.3 S4Vectors_0.42.1
#> [77] ggplot2_3.5.1 sparseMatrixStats_1.16.0
#> [79] munsell_0.5.1 scales_1.3.0
#> [81] glue_1.7.0 lazyeval_0.2.2
#> [83] proxyC_0.4.1 tools_4.4.1
#> [85] data.table_1.15.4 graphlayouts_1.1.1
#> [87] tidygraph_1.3.1 rhdf5_2.48.0
#> [89] grid_4.4.1 tidyr_1.3.1
#> [91] colorspace_2.1-0 SingleCellExperiment_1.26.0
#> [93] nlme_3.1-165 GenomeInfoDbData_1.2.12
#> [95] patchwork_1.2.0 ggforce_0.4.2
#> [97] HDF5Array_1.32.0 cli_3.6.3
#> [99] fansi_1.0.6 segmented_2.1-0
#> [101] S4Arrays_1.4.1 viridisLite_0.4.2
#> [103] dplyr_1.1.4 gtable_0.3.5
#> [105] sass_0.4.9 digest_0.6.36
#> [107] BiocGenerics_0.50.0 SparseArray_1.4.8
#> [109] ggrepel_0.9.5 htmlwidgets_1.6.4
#> [111] farver_2.1.2 memoise_2.0.1
#> [113] htmltools_0.5.8.1 lifecycle_1.0.4
#> [115] httr_1.4.7 statmod_1.5.0
#> [117] MASS_7.3-61