To use CytoMethIC, you need to install the package from Bioconductor. If you don’t have the BiocManager package installed, install it first:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
if (!requireNamespace("CytoMethIC", quietly = TRUE)) {
BiocManager::install("CytoMethIC")
}
CytoMethIC is a comprehensive package that provides model data and functions
for easily using machine learning models that use data from the DNA methylome
to classify cancer type and phenotype from a sample. The primary motivation for
the development of this package is to abstract away the granular and
accessibility-limiting code required to utilize machine learning models in
R. Our package provides this abstraction for RandomForest, e1071 Support
Vector, Extreme Gradient Boosting, and Tensorflow models. This is paired with
an ExperimentHub component, which contains our lab’s models developed for
epigenetic cancer classification and predicting phenotypes. This includes CNS tumor
classification, Pan-cancer classification, race prediction, cell of origin
classification, and subtype classification models.
library(CytoMethIC)
library(ExperimentHub)
library(sesame)
sesameDataCache()
For these examples, we’ll be using models from ExperimentHub and a sample from sesameData.
Table: CytoMethIC supported models
| ModelID | PredictionLabelDescription |
|---|---|
| rfc_cancertype_TCGA33 | TCGA cancer types (N=33) |
| svm_cancertype_TCGA33 | TCGA cancer types (N=33) |
| xgb_cancertype_TCGA33 | TCGA cancer types (N=33) |
| mlp_cancertype_TCGA33 | TCGA cancer types (N=33) |
| rfc_cancertype_CNS66 | CNS Tumor Class (N=66) |
| svm_cancertype_CNS66 | CNS Tumor Class (N=66) |
| xgb_cancertype_CNS66 | CNS Tumor Class (N=66) |
| mlp_cancertype_CNS66 | CNS Tumor Class (N=66) |
| NA | NA |
| NA | NA |
| NA | NA |
The below snippet shows a demonstration of the model abstraction working on random forest and support vector models from CytoMethIC models on ExperimentHub.
cmi_predict(sesameDataGet("HM450.1.TCGA.PAAD")$betas, ExperimentHub()[["EH8395"]],
lift_over=TRUE)
## # A tibble: 1 × 2
## response prob
## <chr> <dbl>
## 1 PAAD 0.852
cmi_predict(sesameDataGet("HM450.1.TCGA.PAAD")$betas, ExperimentHub()[["EH8396"]],
lift_over=TRUE)
## # A tibble: 1 × 2
## response prob
## <chr> <dbl>
## 1 PAAD 0.986
The below snippet shows a demonstration of the cmi_predict function working to predict the subtype of the cancer.
cmi_predict(sesameDataGet("HM450.1.TCGA.PAAD")$betas, ExperimentHub()[["EH8422"]])
## # A tibble: 1 × 2
## response prob
## <chr> <dbl>
## 1 GI.CIN 0.462
The below snippet shows a demonstration of the cmi_predict function working to predict the ethnicity of the patient.
cmi_predict(sesameDataGet("HM450.1.TCGA.PAAD")$betas, ExperimentHub()[["EH8421"]])
## # A tibble: 1 × 2
## response prob
## <chr> <dbl>
## 1 WHITE 0.886
The below snippet shows a demonstration of the cmi_predict function working to predict the cell of origin of the cancer.
cmi_predict(sesameDataGet("HM450.1.TCGA.PAAD")$betas, ExperimentHub()[["EH8423"]])
## # A tibble: 1 × 2
## response prob
## <chr> <dbl>
## 1 C20:Mixed (Stromal/Immune) 0.768
In addition to ExperimentHub Models, this package also supports using models from GitHub URLs. Note that https://github.com/zhou-lab/CytoMethIC_models will be the most frequently updated public repository of our lab’s classifiers.
base_url <- "https://github.com/zhou-lab/CytoMethIC_models/raw/main/models"
cmi_model <- readRDS(url(sprintf("%s/Race3_rfcTCGA_InfHum3.rds", base_url)))
betas <- openSesame(sesameDataGet("EPICv2.8.SigDF")[[1]])
cmi_predict(betas, cmi_model, lift_over=TRUE)
## # A tibble: 1 × 2
## response prob
## <chr> <dbl>
## 1 WHITE 1
sessionInfo()
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.48 sesame_1.25.0 sesameData_1.25.0
## [4] CytoMethIC_1.3.0 ExperimentHub_2.15.0 AnnotationHub_3.15.0
## [7] BiocFileCache_2.15.0 dbplyr_2.5.0 BiocGenerics_0.53.1
## [10] generics_0.1.3
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4
## [3] blob_1.2.4 filelock_1.0.3
## [5] Biostrings_2.75.0 fastmap_1.2.0
## [7] digest_0.6.37 mime_0.12
## [9] lifecycle_1.0.4 KEGGREST_1.47.0
## [11] RSQLite_2.3.7 magrittr_2.0.3
## [13] compiler_4.5.0 rlang_1.1.4
## [15] tools_4.5.0 utf8_1.2.4
## [17] yaml_2.3.10 S4Arrays_1.7.1
## [19] bit_4.5.0 curl_5.2.3
## [21] DelayedArray_0.33.1 plyr_1.8.9
## [23] RColorBrewer_1.1-3 abind_1.4-8
## [25] BiocParallel_1.41.0 withr_3.0.2
## [27] purrr_1.0.2 grid_4.5.0
## [29] stats4_4.5.0 preprocessCore_1.69.0
## [31] fansi_1.0.6 wheatmap_0.2.0
## [33] e1071_1.7-16 colorspace_2.1-1
## [35] ggplot2_3.5.1 MASS_7.3-61
## [37] scales_1.3.0 SummarizedExperiment_1.37.0
## [39] cli_3.6.3 rmarkdown_2.29
## [41] crayon_1.5.3 reshape2_1.4.4
## [43] httr_1.4.7 tzdb_0.4.0
## [45] proxy_0.4-27 DBI_1.2.3
## [47] cachem_1.1.0 stringr_1.5.1
## [49] zlibbioc_1.53.0 parallel_4.5.0
## [51] AnnotationDbi_1.69.0 BiocManager_1.30.25
## [53] XVector_0.47.0 matrixStats_1.4.1
## [55] vctrs_0.6.5 Matrix_1.7-1
## [57] jsonlite_1.8.9 IRanges_2.41.0
## [59] hms_1.1.3 S4Vectors_0.45.0
## [61] bit64_4.5.2 glue_1.8.0
## [63] codetools_0.2-20 stringi_1.8.4
## [65] gtable_0.3.6 BiocVersion_3.21.1
## [67] GenomeInfoDb_1.43.0 GenomicRanges_1.59.0
## [69] UCSC.utils_1.3.0 munsell_0.5.1
## [71] tibble_3.2.1 pillar_1.9.0
## [73] rappdirs_0.3.3 htmltools_0.5.8.1
## [75] randomForest_4.7-1.2 GenomeInfoDbData_1.2.13
## [77] R6_2.5.1 lattice_0.22-6
## [79] evaluate_1.0.1 Biobase_2.67.0
## [81] readr_2.1.5 png_0.1-8
## [83] memoise_2.0.1 BiocStyle_2.35.0
## [85] class_7.3-22 Rcpp_1.0.13-1
## [87] SparseArray_1.7.0 xfun_0.49
## [89] MatrixGenerics_1.19.0 pkgconfig_2.0.3