text: user626: I recently got some EPIC DNA methylation data and I was wondering what are some good practices to follow? 
  I am interested in knowing about normalization and differential analysis. Thank you.
  
  user643: EPIC data can be processed in the same manner as the previous iteration of methylation array data from Illumina (450k). This means that starting with .idat files, normalization should be performed (for example, via the minfi package). A recent paper from the creators of minfi is particularly helpful because it makes clear that normalized EPIC data from their package can be immediately compared against, for example, level 3 TCGA data.
  After that, I suggest using the manifest to attach genomic coordinates to your probes and segregating them into functional regions. By testing differential methylation in only regulatory regions, for example, you can increase the statistical power by reducing the overall number of tests to the ones you expect to yield major differences.
  There are existing packages out there for differential methylation analysis, but without knowing your replicate structure or aims, it is difficult to point you in the right direction.
  
original_text: 626: <p>I recently got some <a href="https://www.illumina.com/products/by-type/microarray-kits/infinium-methylation-epic.html" rel="noreferrer">EPIC DNA methylation</a> data and I was wondering what are some good practices to follow? 
  I am interested in knowing about normalization and differential analysis. Thank you.</p>
   643: <p>EPIC data can be processed in the same manner as the previous iteration of methylation array data from Illumina (450k). This means that starting with .idat files, normalization should be performed (for example, via the minfi package). A <a href="http://biorxiv.org/content/early/2016/07/23/065490" rel="noreferrer">recent paper</a> from the creators of minfi is particularly helpful because it makes clear that normalized EPIC data from their package can be immediately compared against, for example, <a href="https://wiki.nci.nih.gov/display/TCGA/Data+level" rel="noreferrer">level 3 TCGA data</a>.</p>
  
  <p>After that, I suggest using the manifest to attach genomic coordinates to your probes and segregating them into functional regions. By testing differential methylation in only regulatory regions, for example, you can increase the statistical power by reducing the overall number of tests to the ones you expect to yield major differences.</p>
  
  <p>There are existing packages out there for differential methylation analysis, but without knowing your replicate structure or aims, it is difficult to point you in the right direction.</p>
  