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Principal component analysis (PCA) can be invoked on 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 ( 
) and from the Color by drop down list select HPSC. Use the Rotate Mode (
)to explore the plot from different angles, as shown in 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 clear separation of naive and primed HPSC from the cells .
Figure 1. Principal components analysis (PCA) showing methylation profiles of the study samples. Each sample is represented by a dot, the axes are first three PCs, the number in parenthesis indicate the fraction of variance explained by each PC. The number at the top is the variance explained by the first three PCs. The samples are colored by levels of an attribute (HPSC in this example)
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