Batch effect correction software tools | DNA methylation microarray data analysis
Large studies can be particularly susceptible to the effects of unwanted technical variation due to the large number of samples requiring processing. For example, processing may have to occur over several days or be performed by multiple researchers thus increasing the likelihood of technical differences between ‘batches’. Furthermore, unwanted technical variation is often present against a background of unwanted biological variation.
Contains functions for the storage, management and analysis of oligonucleotide arrays. affy provides the user with extreme flexibility when carrying out an analysis and make it possible to access and manipulate probe intensity data. The affy package was designed to balance user control of data analysis with convenience. Graphical user interfaces, object oriented programming and modular function design enhance the convenience of the package.
Allows to remove batch effects and other unwanted variation in high-throughput experiment. SVA is a package containing several functions permitting to identify and build surrogate variables for large data sets. Artifacts can be removed in three ways: (i) identification and estimation of surrogate variables, (ii) direct removal of known batch effect with ComBat and (iii) removal of batch effect with known probes.
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.
An R package for comprehensive analysis of DNA methylation data obtained with any experimental protocol that provides single-CpG resolution, including Infinium 450K microarray and bisulfite sequencing protocols, but also MeDIP-seq and MBD-seq.
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).
Offers an assortment of algorithms dedicated to highlight and correct errors with unknown origin in high-dimensional data. RUV furnishes six different algorithms: RUV-2, 'RUV-4., 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III' to evaluate and adjust unwanted variation using negative controls. This program was primarily developed to process microarray data but can also being extended to various sources of high-dimensional data.
Provides researchers with an easy-to-use and comprehensive interface to the functionality of R and Bioconductor packages for microarray data analysis. As a modular open source project, it allows developers to contribute modules that provide support for additional types of data or extend workflows.