To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("ENmix")

In most cases, you don't need to download the package archive at all.

ENmix

   

This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see ENmix.

Data preprocessing and quality control for Illumina HumanMethylation450 and MethylationEPIC BeadChip

Bioconductor version: 3.4

Illumina Methylation BeadChip array measurements have intrinsic levels of background noise that degrade methylation measurement. The ENmix package provides an efficient data pre-processing tool designed to reduce background noise and improve signal for DNA methylation estimation. Several efficient novel methods were incorporated in the package: ENmix is a model based background correction method that can significantly improve accuracy and reproducibility of methylation measures; RCP taking advantage of the high spatial correlation of DNA methylation levels between nearby type I and II probe pairs to reduce probe type bias and improve data quality on type II probe measures.The data structure used by the ENmix package is compatible with several other related R packages, such as minfi, wateRmelon and ChAMP, providing straightforward integration of ENmix-corrected datasets for subsequent data analysis. The software is designed to support large scale data analysis, and provides multi-processor parallel computing wrappers for some commonly used but computation intensive data preprocessing methods. In addition ENmix package has selectable complementary functions for efficient data visualization (such as data distribution plotting), quality control (identification and filtering of low quality data points, samples, probes, and outliers, along with imputation of missing values), inter-array normalization (3 different quantile normalizations), identification of probes with multimodal distributions due to SNPs and other factors, and exploration of data variance structure using principal component regression analysis plots. Together these provide a set of flexible and transparent tools for preprocessing of EWAS data in a computationally-efficient and user-friendly package.

Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb]

Maintainer: Zongli Xu <xuz at niehs.nih.gov>

Citation (from within R, enter citation("ENmix")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("ENmix")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("ENmix")

 

PDF R Script ENmix User's Guide
PDF   Reference Manual
Text   NEWS

Details

biocViews BatchEffect, DNAMethylation, DataImport, MethylationArray, Microarray, Normalization, OneChannel, Preprocessing, PrincipalComponent, QualityControl, Regression, Software, TwoChannel
Version 1.10.0
In Bioconductor since BioC 3.1 (R-3.2) (2 years)
License Artistic-2.0
Depends minfi, parallel, doParallel, Biobase(>= 2.17.8), foreach
Imports MASS, preprocessCore, wateRmelon, sva, geneplotter, impute, grDevices, graphics, stats
LinkingTo
Suggests minfiData(>= 0.4.1), RPMM, RUnit, BiocGenerics
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source ENmix_1.10.0.tar.gz
Windows Binary ENmix_1.10.0.zip
Mac OS X 10.9 (Mavericks) ENmix_1.10.0.tgz
Subversion source (username/password: readonly)
Git source https://github.com/Bioconductor-mirror/ENmix/tree/release-3.4
Package Short Url http://bioconductor.org/packages/ENmix/
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