PGS Documentation

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

  • Select PCA Scatter Plot from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Scatter Plot tab
  • Select 2. Cell Type for Color by  
  • Select 3. Gender for Size by
  • Select (Image Addedto 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 clear separation of naive and primed HPSC from the cells transduced with short hairpin (sh) RNA lentiviruses (shNANOG and shPOU5F1).Two of the LCLs samples do not cluster with the others, but we will not exclude them for this tutorial. 

 

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SubtitleTextPrincipal 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

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2. Cell Type
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Annotated PCA plot

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Next, distribution of M-beta values across the samples can also be inspected by a box-and-whiskers plot: QA/QC > Plot Sample Box & Whiskers Chart.

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

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

 

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SubtitleTextBox and whiskers plot showing distribution of M-values (y-axis) across the study samples (x-axis). Samples are colored by a categorical attribute (

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Cell Type). The middle line is the median, box represents the upper and the lower quartile, while the whiskers correspond to the 90th and 10th percentile of the data
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 An alternative way to take a look at the distribution of Mbeta-values is a histogram (QA/QC > Plot Sample Histogram).

  • Select 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 3).

 

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SubtitleTextSample histogram. Each sample is a line, M-beta values are on the horizontal axis and their frequencies on the vertical axis. Two peaks correspond to two probe types (I and II) present on the MethylationEPIC array. Sample colors correspond to a categorical attribute (i.e. HPSCCell Type)
AnchorNamesample histogram

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