Principal component analysis (PCA) can be invoked on performed to visualize clusters in the methylation data to reveal clustering of the samples, but also as a quality control procedure (detection of outliers could point to possible low quality or mislabeled samples). To obtain the PCA plot, switch to the Scatter Plot tab, push Recompute ( Image Removed ) and from the Color by drop down list select HPSC. Use the Rotate Mode (Image Removed )to explore the plot from different angles, as seen in Figure 1. Each as outliers within a group could suggest poor data quality, batch effects, mislabeled samples, or uninformative groupings.

• Select Plot PCA Scatter Plot from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Scatter Plot tab
• Select 3. shRNA for Color by and 2. State for Size by 
• Select (Image Added) to enable Rotate Mode
• Left click and drag to rotate the plot and view different angles (Figure 1)

Each dot of the plot is a single sample and represents the average methylation status across all CpG loci. The result is shown in the demonstrating We can see clear separation of naive and primed HPSC hPSCs from the cells naive hPSCs transduced with short hairpin (sh) RNA shRNA lentiviruses (shNANOG and shPOU5F1). One shNANOG sample might be an outlier, but we will not exclude it for this tutorial. 

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

• Select Plot Sample Box

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• and Whiskers Chart from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Scatter Plot tab

Each box-and-whisker is a sample and the y-axis shows M-valuesvalue ranges. Samples in this data set seem reasonably uniform and no outliers can be detected (Figure 2).

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 An alternative way to take a look at the distribution of M-values is a histogram (QA/QC > Plot Sample Histogram). . 

• Select Plot Sample Histogram from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Histogram tab

Again, no sample in the tutorial data set stands out (Figure 34).

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