Performing scClassify using pretrained model
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.
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:
We can also visualise the cell type tree of the reference data.
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:
Session Info
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] scClassify_1.19.0 BiocStyle_2.35.0
#>
#> 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_4.4.2 mgcv_1.9-1
#> [7] DelayedMatrixStats_1.29.0 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 XVector_0.47.0
#> [15] labeling_0.4.3 ggraph_2.2.1
#> [17] rmarkdown_2.29 UCSC.utils_1.3.0
#> [19] purrr_1.0.2 xfun_0.49
#> [21] zlibbioc_1.52.0 cachem_1.1.0
#> [23] GenomeInfoDb_1.43.2 jsonlite_1.8.9
#> [25] rhdf5filters_1.19.0 DelayedArray_0.33.3
#> [27] Rhdf5lib_1.29.0 BiocParallel_1.41.0
#> [29] tweenr_2.0.3 parallel_4.4.2
#> [31] cluster_2.1.8 R6_2.5.1
#> [33] bslib_0.8.0 stringi_1.8.4
#> [35] limma_3.63.2 diptest_0.77-1
#> [37] GenomicRanges_1.59.1 jquerylib_0.1.4
#> [39] Rcpp_1.0.13-1 SummarizedExperiment_1.37.0
#> [41] knitr_1.49 mixtools_2.0.0
#> [43] IRanges_2.41.2 Matrix_1.7-1
#> [45] splines_4.4.2 igraph_2.1.2
#> [47] tidyselect_1.2.1 abind_1.4-8
#> [49] yaml_2.3.10 hopach_2.67.0
#> [51] viridis_0.6.5 codetools_0.2-20
#> [53] minpack.lm_1.2-4 Cepo_1.13.0
#> [55] lattice_0.22-6 tibble_3.2.1
#> [57] plyr_1.8.9 Biobase_2.67.0
#> [59] withr_3.0.2 evaluate_1.0.1
#> [61] survival_3.8-3 proxy_0.4-27
#> [63] polyclip_1.10-7 kernlab_0.9-33
#> [65] pillar_1.10.0 BiocManager_1.30.25
#> [67] MatrixGenerics_1.19.0 stats4_4.4.2
#> [69] plotly_4.10.4 generics_0.1.3
#> [71] S4Vectors_0.45.2 ggplot2_3.5.1
#> [73] sparseMatrixStats_1.19.0 munsell_0.5.1
#> [75] scales_1.3.0 glue_1.8.0
#> [77] lazyeval_0.2.2 proxyC_0.4.1
#> [79] maketools_1.3.1 tools_4.4.2
#> [81] data.table_1.16.4 sys_3.4.3
#> [83] buildtools_1.0.0 graphlayouts_1.2.1
#> [85] tidygraph_1.3.1 rhdf5_2.51.1
#> [87] grid_4.4.2 tidyr_1.3.1
#> [89] colorspace_2.1-1 SingleCellExperiment_1.29.1
#> [91] nlme_3.1-166 GenomeInfoDbData_1.2.13
#> [93] patchwork_1.3.0 ggforce_0.4.2
#> [95] HDF5Array_1.35.2 cli_3.6.3
#> [97] segmented_2.1-3 S4Arrays_1.7.1
#> [99] viridisLite_0.4.2 dplyr_1.1.4
#> [101] gtable_0.3.6 sass_0.4.9
#> [103] digest_0.6.37 BiocGenerics_0.53.3
#> [105] SparseArray_1.7.2 ggrepel_0.9.6
#> [107] htmlwidgets_1.6.4 farver_2.1.2
#> [109] memoise_2.0.1 htmltools_0.5.8.1
#> [111] lifecycle_1.0.4 httr_1.4.7
#> [113] statmod_1.5.0 MASS_7.3-61