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 4.4.1 (2024-06-14)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Ventura 13.6.7
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> 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_1.18.0 BiocStyle_2.34.0
#>
#> loaded via a namespace (and not attached):
#> [1] gridExtra_2.3 rlang_1.1.4
#> [3] magrittr_2.0.3 matrixStats_1.4.1
#> [5] compiler_4.4.1 mgcv_1.9-1
#> [7] DelayedMatrixStats_1.28.0 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_2.8.5
#> [15] XVector_0.46.0 labeling_0.4.3
#> [17] ggraph_2.2.1 utf8_1.2.4
#> [19] rmarkdown_2.29 UCSC.utils_1.2.0
#> [21] tinytex_0.54 purrr_1.0.2
#> [23] xfun_0.49 zlibbioc_1.52.0
#> [25] cachem_1.1.0 GenomeInfoDb_1.42.0
#> [27] jsonlite_1.8.9 rhdf5filters_1.18.0
#> [29] DelayedArray_0.32.0 Rhdf5lib_1.28.0
#> [31] BiocParallel_1.40.0 tweenr_2.0.3
#> [33] parallel_4.4.1 cluster_2.1.6
#> [35] R6_2.5.1 bslib_0.8.0
#> [37] stringi_1.8.4 limma_3.62.1
#> [39] diptest_0.77-1 GenomicRanges_1.58.0
#> [41] jquerylib_0.1.4 Rcpp_1.0.13-1
#> [43] bookdown_0.41 SummarizedExperiment_1.36.0
#> [45] knitr_1.49 mixtools_2.0.0
#> [47] IRanges_2.40.0 Matrix_1.7-1
#> [49] splines_4.4.1 igraph_2.1.1
#> [51] tidyselect_1.2.1 abind_1.4-8
#> [53] yaml_2.3.10 hopach_2.66.0
#> [55] viridis_0.6.5 codetools_0.2-20
#> [57] minpack.lm_1.2-4 Cepo_1.12.0
#> [59] lattice_0.22-6 tibble_3.2.1
#> [61] plyr_1.8.9 Biobase_2.66.0
#> [63] withr_3.0.2 evaluate_1.0.1
#> [65] survival_3.7-0 proxy_0.4-27
#> [67] polyclip_1.10-7 kernlab_0.9-33
#> [69] pillar_1.9.0 BiocManager_1.30.25
#> [71] MatrixGenerics_1.18.0 stats4_4.4.1
#> [73] plotly_4.10.4 generics_0.1.3
#> [75] S4Vectors_0.44.0 ggplot2_3.5.1
#> [77] sparseMatrixStats_1.18.0 munsell_0.5.1
#> [79] scales_1.3.0 glue_1.8.0
#> [81] lazyeval_0.2.2 proxyC_0.4.1
#> [83] tools_4.4.1 data.table_1.16.2
#> [85] graphlayouts_1.2.0 tidygraph_1.3.1
#> [87] rhdf5_2.50.0 grid_4.4.1
#> [89] tidyr_1.3.1 colorspace_2.1-1
#> [91] SingleCellExperiment_1.28.0 nlme_3.1-166
#> [93] GenomeInfoDbData_1.2.13 patchwork_1.3.0
#> [95] ggforce_0.4.2 HDF5Array_1.34.0
#> [97] cli_3.6.3 fansi_1.0.6
#> [99] segmented_2.1-3 S4Arrays_1.6.0
#> [101] viridisLite_0.4.2 dplyr_1.1.4
#> [103] gtable_0.3.6 sass_0.4.9
#> [105] digest_0.6.37 BiocGenerics_0.52.0
#> [107] SparseArray_1.6.0 ggrepel_0.9.6
#> [109] htmlwidgets_1.6.4 farver_2.1.2
#> [111] memoise_2.0.1 htmltools_0.5.8.1
#> [113] lifecycle_1.0.4 httr_1.4.7
#> [115] statmod_1.5.0 MASS_7.3-61