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Normalization software tools | DNA methylation microarray data analysis

Normalisation concerns the removal of sources of experimental artefacts, random noise and technical and systematic variation caused by microarray technology, which, if left unaddressed, has the potential to mask true biological differences (Sun et al, 2011a). Two different types of normalisation exist: (1) between-array normalisation, removing technical artefacts between samples on different arrays, and (2) within-array normalisation, correcting for intensity-related dye biases (Siegmund, 2011).

References:
(Sun et al., 2011) Preprocessing differential methylation hybridization microarray data. BioData Min.
(Siegmund, 2011) Statistical approaches for the analysis of DNA methylation microarray data. Hum Genet.

Source text:
(Wilhelm-Benartzi et al., 2013) Review of processing and analysis methods for DNA methylation array data. Br J Cancer.

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missMethyl
Performs normalisation, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array. The functions have been written to complement the limma package and are compatible with data objects from minfi, methylumi and edgeR. Detects differential variability for individual CpG sites in methylation data. DiffVar is available as a function in the missMethyl Bioconductor R package, and depends on the limma framework.
ChAMP
Allows Illumina HumanMethylation BeadChip analysis. ChAMP is an integrated analysis pipeline including functions for (i) filtering low quality probes, adjustment for Infinium I and Infinium II probe design, (ii) batch effect correction, detecting differentially methylated positions (DMPs), (iii) finding differentially methylated regions (DMRs) and (iv) detection of copy number aberrations. The software also allows detection of differentially methylated genomic blocks (DMB) and Gene Set Enrichment Analysis (GSEA).
lumi
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.
SWAN / Subset-quantile Within Array Normalization
A within array normalization method that substantially reduces the technical variability between the probe types whilst maintaining the important biological differences. The SWAN method makes the assumption that the number of CpGs within the 50 bp probe sequence reflects the underlying biology of the region being interrogated. Hence, the overall distribution of intensities of probes with the same number of CpGs in the probe body should be the same. The method then uses a subset quantile normalization approach to adjust the intensities of the probes on the arrays.
Methylumi
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.
minfi
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.
funtooNorm
An R package for normalization of data from the Illumina Infinium Human Methylation450 BeadChip (Illumina 450 K) built on the concepts in the recently published funNorm method, and introducing cell-type or tissue-type flexibility. funtooNorm is relevant for data sets containing samples from two or more cell or tissue types. A visual display of cross-validated errors informs the choice of the optimal number of components in the normalization. Improved normalization of datasets containing multiple tissues can be expected to translate into increased power to detect associations of interest, due to the inferred reduction in residual error; funNorm and this extension funtooNorm are designed with this goal in mind.
Meffil
Proceeds quality control, normalizes and performs epigenome-wide association studies (EWAS). Meffil permits to minimize computational memory requirements to 5% of that required by other R packages excluding increasing running time. It is based on a reimplementation of functional normalization method. The tool normalizes datasets distributed across physically different locations, avoiding sharing any biologically-based individual-level data. In addition, it can reduce heterogeneity in meta-analyses of EWAS.
ADMIRE / Analysis of DNA methylation in genomic regions
Allows to analyze and visualize differential methylation in genomic regions. ADMIRE is a semi-automatic pipeline that features five different normalization methods and performs two one-sided two-sample rank tests (Mann–Whitney U tests). The software features arbitrary experimental settings, quality control, automatic filtering, normalization, multiple testing, differential analyses on arbitrary genomic regions. It additionally implements a gene set enrichment procedure.
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