Performing scClassify using pretrained model
26 October 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: 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 4.1.1 (2021-08-10)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.14-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_1.6.0 BiocStyle_2.22.0
#>
#> loaded via a namespace (and not attached):
#> [1] segmented_1.3-4 nlme_3.1-153
#> [3] bitops_1.0-7 matrixStats_0.61.0
#> [5] hopach_2.54.0 GenomeInfoDb_1.30.0
#> [7] tools_4.1.1 bslib_0.3.1
#> [9] utf8_1.2.2 R6_2.5.1
#> [11] HDF5Array_1.22.0 mgcv_1.8-38
#> [13] DBI_1.1.1 BiocGenerics_0.40.0
#> [15] colorspace_2.0-2 rhdf5filters_1.6.0
#> [17] gridExtra_2.3 tidyselect_1.1.1
#> [19] proxyC_0.2.1 compiler_4.1.1
#> [21] Biobase_2.54.0 DelayedArray_0.20.0
#> [23] labeling_0.4.2 bookdown_0.24
#> [25] sass_0.4.0 diptest_0.76-0
#> [27] scales_1.1.1 proxy_0.4-26
#> [29] stringr_1.4.0 digest_0.6.28
#> [31] mixtools_1.2.0 rmarkdown_2.11
#> [33] XVector_0.34.0 pkgconfig_2.0.3
#> [35] htmltools_0.5.2 sparseMatrixStats_1.6.0
#> [37] Cepo_1.0.0 MatrixGenerics_1.6.0
#> [39] highr_0.9 fastmap_1.1.0
#> [41] limma_3.50.0 rlang_0.4.12
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#> [49] dplyr_1.0.7 RCurl_1.98-1.5
#> [51] magrittr_2.0.1 GenomeInfoDbData_1.2.7
#> [53] patchwork_1.1.1 Matrix_1.3-4
#> [55] Rcpp_1.0.7 munsell_0.5.0
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#> [61] lifecycle_1.0.1 stringi_1.7.5
#> [63] yaml_2.2.1 ggraph_2.0.5
#> [65] MASS_7.3-54 SummarizedExperiment_1.24.0
#> [67] zlibbioc_1.40.0 rhdf5_2.38.0
#> [69] plyr_1.8.6 grid_4.1.1
#> [71] parallel_4.1.1 ggrepel_0.9.1
#> [73] crayon_1.4.1 lattice_0.20-45
#> [75] splines_4.1.1 graphlayouts_0.7.1
#> [77] magick_2.7.3 knitr_1.36
#> [79] pillar_1.6.4 igraph_1.2.7
#> [81] GenomicRanges_1.46.0 reshape2_1.4.4
#> [83] stats4_4.1.1 glue_1.4.2
#> [85] evaluate_0.14 RcppParallel_5.1.4
#> [87] BiocManager_1.30.16 vctrs_0.3.8
#> [89] tweenr_1.0.2 gtable_0.3.0
#> [91] purrr_0.3.4 polyclip_1.10-0
#> [93] tidyr_1.1.4 kernlab_0.9-29
#> [95] assertthat_0.2.1 ggplot2_3.3.5
#> [97] xfun_0.27 ggforce_0.3.3
#> [99] tidygraph_1.2.0 survival_3.2-13
#> [101] viridisLite_0.4.0 minpack.lm_1.2-1
#> [103] SingleCellExperiment_1.16.0 tibble_3.1.5
#> [105] IRanges_2.28.0 cluster_2.1.2
#> [107] statmod_1.4.36 ellipsis_0.3.2