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Provides class infrastructure and associated methods to construct an Illumina analysis workflow pipeline starting with raw data through functional analysis. Besides supporting the existing algorithms for microarray data, the lumi package includes several unique parts: (i) a variance-stabilizing transformation that utilizes the technical replicates available on the Illumina microarray; (ii) normalization algorithms designed for Illumina microarray data and; iii) the nucleotide universal identifier annotation packages.


A package that provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An ``intelligent'' import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included.


A suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. minfi provides methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. Several preprocessing algorithms are available and the infrastructure provides a convenient way for developers to easily implement their techniques as Bioconductor tools. By making SNP annotation available, users can choose to be cautious about probes that may behave unexpectedly due to the inclusion of a SNP in the probe sequence. minfi is unique in that it provides both bump hunting and block finding capabilities, and the assessment of statistical significance for the identified regions. Finally, because the package is implemented in Bioconductor, it gives users access to the countless analysis and visualization tools available in R.


Provides a background correction method which uses a mixture of exponential and truncated normal distributions to flexibly model signal intensity and uses a truncated normal distribution to model background noise. Depending on data availability, three approaches are employed to estimate background normal distribution parameters using (i) internal chip negative controls, (ii) out-of-band Infinium I probe intensities or (iii) combined methylated and unmethylated intensities. Evaluation results in both duplicates and experimental standard samples showed that ENmix outperformed commonly used background subtraction methods in terms of improvement in replicability and accuracy as well as reducing probe design bias. After ENmix background correction the resulting data can be used with other commonly-used preprocessing methods including quantile normalization for between-sample normalization and BMIQ for further correction of probe-design bias.


Allows visualization and analysis of data generated on Illumina array platforms. GenomeStudio is a data analysis tool that provides three modules: (1) Genotyping Module for the analysis of single nucleotide polymorphism (SNPs) and copy number variations (CNVs) data and detection of sample outliers, (2) Gene Expression Module for the detection of cytosine methylation at single-base resolution and identification of methylation signatures across the entire genome, and (3) Polyploid Genotyping Module for the analysis of polyploid organism genotyping data.