This is the development version of CytoMethIC; for the stable release version, see CytoMethIC.
DNA methylation-based machine learning modelsBioconductor version: Development (3.22)
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] ORCID: 0000-0001-9126-1932 , Jacob Fanale [aut, cre] ORCID: 0009-0002-0490-3269
Maintainer: Jacob Fanale <jfanale at seas.upenn.edu>
Citation (from within R, entercitation("CytoMethIC")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("CytoMethIC")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CytoMethIC")Follow Installation instructions to use this package in your R session.
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