Performing scClassify using pretrained model
1 November 2022
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 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
#>
#> 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
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] scClassify_1.10.0 BiocStyle_2.26.0
#>
#> loaded via a namespace (and not attached):
#> [1] segmented_1.6-0 nlme_3.1-160
#> [3] bitops_1.0-7 matrixStats_0.62.0
#> [5] hopach_2.58.0 GenomeInfoDb_1.34.0
#> [7] tools_4.2.1 bslib_0.4.0
#> [9] utf8_1.2.2 R6_2.5.1
#> [11] HDF5Array_1.26.0 mgcv_1.8-41
#> [13] DBI_1.1.3 BiocGenerics_0.44.0
#> [15] colorspace_2.0-3 rhdf5filters_1.10.0
#> [17] withr_2.5.0 tidyselect_1.2.0
#> [19] gridExtra_2.3 proxyC_0.3.3
#> [21] compiler_4.2.1 cli_3.4.1
#> [23] Biobase_2.58.0 DelayedArray_0.24.0
#> [25] labeling_0.4.2 bookdown_0.29
#> [27] sass_0.4.2 diptest_0.76-0
#> [29] scales_1.2.1 proxy_0.4-27
#> [31] stringr_1.4.1 digest_0.6.30
#> [33] mixtools_1.2.0 rmarkdown_2.17
#> [35] XVector_0.38.0 pkgconfig_2.0.3
#> [37] htmltools_0.5.3 sparseMatrixStats_1.10.0
#> [39] Cepo_1.4.0 MatrixGenerics_1.10.0
#> [41] highr_0.9 fastmap_1.1.0
#> [43] limma_3.54.0 rlang_1.0.6
#> [45] DelayedMatrixStats_1.20.0 jquerylib_0.1.4
#> [47] generics_0.1.3 farver_2.1.1
#> [49] jsonlite_1.8.3 BiocParallel_1.32.0
#> [51] dplyr_1.0.10 RCurl_1.98-1.9
#> [53] magrittr_2.0.3 GenomeInfoDbData_1.2.9
#> [55] patchwork_1.1.2 Matrix_1.5-1
#> [57] Rcpp_1.0.9 munsell_0.5.0
#> [59] S4Vectors_0.36.0 Rhdf5lib_1.20.0
#> [61] fansi_1.0.3 viridis_0.6.2
#> [63] lifecycle_1.0.3 stringi_1.7.8
#> [65] yaml_2.3.6 ggraph_2.1.0
#> [67] MASS_7.3-58.1 SummarizedExperiment_1.28.0
#> [69] zlibbioc_1.44.0 rhdf5_2.42.0
#> [71] plyr_1.8.7 grid_4.2.1
#> [73] parallel_4.2.1 ggrepel_0.9.1
#> [75] lattice_0.20-45 splines_4.2.1
#> [77] graphlayouts_0.8.3 magick_2.7.3
#> [79] knitr_1.40 pillar_1.8.1
#> [81] igraph_1.3.5 GenomicRanges_1.50.0
#> [83] reshape2_1.4.4 codetools_0.2-18
#> [85] stats4_4.2.1 glue_1.6.2
#> [87] evaluate_0.17 RcppParallel_5.1.5
#> [89] BiocManager_1.30.19 vctrs_0.5.0
#> [91] tweenr_2.0.2 gtable_0.3.1
#> [93] purrr_0.3.5 polyclip_1.10-4
#> [95] tidyr_1.2.1 kernlab_0.9-31
#> [97] assertthat_0.2.1 cachem_1.0.6
#> [99] ggplot2_3.3.6 xfun_0.34
#> [101] ggforce_0.4.1 tidygraph_1.2.2
#> [103] survival_3.4-0 viridisLite_0.4.1
#> [105] minpack.lm_1.2-2 SingleCellExperiment_1.20.0
#> [107] tibble_3.1.8 IRanges_2.32.0
#> [109] cluster_2.1.4 statmod_1.4.37