Include quality control report in output:

If you check this option, a PDF file will be created containing the output of minfi's quality control routines.

Select a method for input normalization:

Normalization is used to remove unwanted variation and normalize between arrays. You can choose from five different normalization methods:

  1. Functional normalization
  2. Noob normalization
  3. SWAN normalization
  4. Quantile normalization
  5. Illumina Genome Studio normalization

Additionaly, you can skip the normalization step by selecting No normalization - use raw values.

Detection p-value threshold for failed probe identification:

Every probe on the array has a detection p-value assigned, which indicates confidence of the scanner that the detection was correct. A probe will be marked as failed in a sample if its detection p-value is higher that the given value. Together with the

Failed sample threshold:

You can subsequently exclude probes that have a certain proportion of failed marks across all samples.

Q-value cutoff for multiple testing:

Multiple testing is done automatically by ADMIRE and corrects the test statistic for multiple performed tests. If a certain region remains with a Q-value or FDR below the given value, it will be retained for subsequent analysis, like the gene set enrichment analysis or visualizations.

Number of additional plots for n best regions:

The number given here will determine how many visualizations are plotted for significant regions. Importantly, if the number of samples is higher than 100, a heatmap is created. Otherwise, non-proportional bubble plots are plotted.

Select genomic regions to test:

Regions selected here will be overlapped with methylation probes and significant different methylated regions will be reported.

By uploading bed files to the work space (right panel), users can give custom regions by adding them to the custom genomic regions list.

Choose gene sets:

If a gene set is given (either by selecting pre-defined gene sets or uploading a custom gene set), a gene set enrichment analysis is performed by taking all significantly different methylathed regions that are annotated with a gene name and testing them for enrichment in a gene set. Regions with annotated gene names are Promoter Regions (2kB) and Exons.