Principal component analysis (PCA) can be performed to visualize clusters in the methylation data, but also serves as a quality control procedure; outliers within a group could suggest poor data quality, batch effects, mislabeled samples, or uninformative groupings.

• Select female_only in the spreadsheet tree
• Select Plot PCA Scatter Plot from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Scatter Plot tab
• Select 2. Experimental Group for Color by  
• Select () to enable Rotate Mode
• Left click and drag to rotate the plot and view different angles (Figure 1)

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Next, distribution of M-values across the samples can also be inspected by a box-and-whiskers plot. 

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An alternative way to take a look at the distribution of M-values is a histogram. 

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