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. 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.
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 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.
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.
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.
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).