Package Recipe 'bioconductor-cytomethic'

recipe bioconductor-cytomethic

DNA methylation-based machine learning models

Homepage:: https://bioconductor.org/packages/3.22/data/experiment/html/CytoMethIC.html
License:: Artistic-2.0
Recipe:: /bioconductor-cytomethic/meta.yaml

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.

package bioconductor-cytomethic¶

Versions:

1.6.0-0, 1.2.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-data-packages >=20260207
on bioconductor-experimenthub >=3.0.0,<3.1.0
on bioconductor-sesame >=1.28.0,<1.29.0
on bioconductor-sesamedata >=1.28.0,<1.29.0
on curl
on r-base >=4.5,<4.6.0a0
on r-biocmanager

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-cytomethic

to add into an existing workspace instead, run:

pixi add bioconductor-cytomethic

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-cytomethic

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-cytomethic

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-cytomethic:<tag>

(see bioconductor-cytomethic/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-cytomethic/README.html)