Package: ENmix
Version: 1.10.0
Date: 2016-07-28
Title: Data preprocessing and quality control for Illumina
        HumanMethylation450 and MethylationEPIC BeadChip
Type: Package
Authors@R: c(person("Zongli","Xu",role=c("cre","aut"),email="xuz@niehs.nih.gov"),
    person("Liang","Niu",role=c("aut"),email="niulg@ucmail.uc.edu"),
    person("Leping","Li",role=c("ctb"),email="li3@niehs.nih.gov"),
    person("Jack","Taylor",role=c("ctb"),email="taylor@niehs.nih.gov"))
Description: 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.
Depends: minfi,parallel,doParallel,Biobase (>= 2.17.8),foreach
Imports:
        MASS,preprocessCore,wateRmelon,sva,geneplotter,impute,grDevices,graphics,stats
Suggests: minfiData (>= 0.4.1), RPMM, RUnit, BiocGenerics
biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel,
        Microarray, OneChannel, MethylationArray, BatchEffect,
        Normalization, DataImport, Regression, PrincipalComponent
License: Artistic-2.0
NeedsCompilation: no
Maintainer: Zongli Xu <xuz@niehs.nih.gov>
Packaged: 2016-10-18 00:17:49 UTC; biocbuild
Author: Zongli Xu [cre, aut],
  Liang Niu [aut],
  Leping Li [ctb],
  Jack Taylor [ctb]
Built: R 3.3.1; ; 2016-10-18 02:13:31 UTC; windows
