Positional effect correction software tools | DNA methylation microarray data analysis
Filtering out probes with potential errors and adjusting experimental bias have been necessary data pre-processing steps. There is also ‘positional effects’ that the same sample in different physical positions on the array could be measured as different methylation levels. The earliest mention of the positional effect in the Illumina gene expression microarray analysis did not provide a method for correction except an advisement to randomly set the samples in the array.
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.
Removes the positional effects and batch effects in data preprocessing. posibatch is based on the ComBat function. The tool uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for positional effects and batch effects. It returns an expression or methylation matrix that has been corrected for position effects and batch effects.
Adjusting batch effects in microarray expression data using Empirical Bayes methods. The modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a ‘gold-standard’ batch.
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