CytoMethIC
This is the released version of CytoMethIC; for the devel version, see CytoMethIC.
DNA methylation-based machine learning models
Bioconductor version: Release (3.23)
This package 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 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. The package links to our models on ExperimentHub. The package currently supports HM450, EPIC, EPICv2, MSA, and MM285.
Author: Wanding Zhou [aut] 
, Jacob Fanale [aut, cre]
Maintainer: Jacob Fanale <jfanale at seas.upenn.edu>
citation("CytoMethIC")):Wanding Zhou, Jacob Fanale. CytoMethIC: DNA methylation-based machine learning models. doi:10.18129/B9.bioc.CytoMethIC, R package version 1.8.0, https://bioconductor.org/packages/CytoMethIC.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CytoMethIC")For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CytoMethIC")| 1. Basic Information | HTML | R Script |
| 2. CytoMethIC Oncology | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | CancerData, ExperimentData, ExperimentHub, Genome, MethylationArrayData, MicroarrayData, PackageTypeData |
| Version | 1.8.0 |
| In Bioconductor since | BioC 3.19 (R-4.4) (2.5 years) |
| License | Artistic-2.0 |
| Depends | R (>= 4.4.0), ExperimentHub |
| Imports | utils, stats, tools, sesame, methods, sesameData, BiocParallel, BiocManager |
| System Requirements | |
| URL | https://github.com/zhou-lab/CytoMethIC |
| Bug Reports | https://github.com/zhou-lab/CytoMethIC/issues |
See More
| Suggests | tibble, BiocStyle, randomForest, testthat, knitr, rmarkdown, e1071, xgboost, keras, tensorflow |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report, r-universe |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | CytoMethIC_1.8.0.tar.gz |
| Windows Binary (x86_64) | |
| macOS Binary (big-sur-x86_64) | |
| macOS Binary (sonoma-arm64) | |
| Source Repository | git clone https://git.bioconductor.org/packages/CytoMethIC |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/CytoMethIC |
| Package Short Url | https://bioconductor.org/packages/CytoMethIC/ |
| Package Downloads Report | Download Stats |