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")
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))
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
sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.6.1
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
#>
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] scClassify_1.14.0 BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 gridExtra_2.3
#> [3] rlang_1.1.1 magrittr_2.0.3
#> [5] matrixStats_1.0.0 compiler_4.3.1
#> [7] mgcv_1.9-0 DelayedMatrixStats_1.24.0
#> [9] vctrs_0.6.3 reshape2_1.4.4
#> [11] stringr_1.5.0 pkgconfig_2.0.3
#> [13] crayon_1.5.2 fastmap_1.1.1
#> [15] magick_2.7.4 XVector_0.42.0
#> [17] labeling_0.4.2 ggraph_2.1.0
#> [19] utf8_1.2.3 rmarkdown_2.23
#> [21] purrr_1.0.1 xfun_0.39
#> [23] zlibbioc_1.48.0 cachem_1.0.8
#> [25] GenomeInfoDb_1.38.0 jsonlite_1.8.7
#> [27] highr_0.10 rhdf5filters_1.14.0
#> [29] DelayedArray_0.28.0 Rhdf5lib_1.24.0
#> [31] BiocParallel_1.36.0 tweenr_2.0.2
#> [33] parallel_4.3.1 cluster_2.1.4
#> [35] R6_2.5.1 bslib_0.5.0
#> [37] stringi_1.7.12 limma_3.58.0
#> [39] diptest_0.76-0 GenomicRanges_1.54.0
#> [41] jquerylib_0.1.4 Rcpp_1.0.11
#> [43] bookdown_0.34 SummarizedExperiment_1.32.0
#> [45] knitr_1.43 mixtools_2.0.0
#> [47] IRanges_2.36.0 Matrix_1.6-0
#> [49] splines_4.3.1 igraph_1.5.0
#> [51] tidyselect_1.2.0 abind_1.4-5
#> [53] yaml_2.3.7 hopach_2.62.0
#> [55] viridis_0.6.3 codetools_0.2-19
#> [57] minpack.lm_1.2-3 Cepo_1.8.0
#> [59] lattice_0.21-8 tibble_3.2.1
#> [61] plyr_1.8.8 Biobase_2.62.0
#> [63] withr_2.5.0 evaluate_0.21
#> [65] survival_3.5-5 RcppParallel_5.1.7
#> [67] proxy_0.4-27 polyclip_1.10-4
#> [69] kernlab_0.9-32 pillar_1.9.0
#> [71] BiocManager_1.30.22 MatrixGenerics_1.14.0
#> [73] stats4_4.3.1 plotly_4.10.2
#> [75] generics_0.1.3 RCurl_1.98-1.12
#> [77] S4Vectors_0.40.1 ggplot2_3.4.2
#> [79] sparseMatrixStats_1.14.0 munsell_0.5.0
#> [81] scales_1.2.1 glue_1.6.2
#> [83] lazyeval_0.2.2 proxyC_0.3.3
#> [85] tools_4.3.1 data.table_1.14.8
#> [87] graphlayouts_1.0.0 tidygraph_1.2.3
#> [89] rhdf5_2.46.0 grid_4.3.1
#> [91] tidyr_1.3.0 colorspace_2.1-0
#> [93] SingleCellExperiment_1.24.0 nlme_3.1-162
#> [95] GenomeInfoDbData_1.2.10 patchwork_1.1.2
#> [97] ggforce_0.4.1 HDF5Array_1.30.0
#> [99] cli_3.6.1 fansi_1.0.4
#> [101] segmented_1.6-4 S4Arrays_1.2.0
#> [103] viridisLite_0.4.2 dplyr_1.1.2
#> [105] gtable_0.3.3 sass_0.4.6
#> [107] digest_0.6.33 BiocGenerics_0.48.0
#> [109] SparseArray_1.2.0 ggrepel_0.9.3
#> [111] htmlwidgets_1.6.2 farver_2.1.1
#> [113] htmltools_0.5.5 lifecycle_1.0.3
#> [115] httr_1.4.6 statmod_1.5.0
#> [117] MASS_7.3-60