Preprocessing

We considered every β value to be unreliable when its corresponding detection p-value is not below the threshold T:

p ≥ T = 0.05

The figure below summarizes the observed number of unreliable measurements per probe and per sample.

Number of values per

probe sample

Figure 1

Cumulative distribution function of number of unreliable values per probe/sample.

RnBeads executed Greedycut using the threshold given above and applied all its steps. Briefly, Greedycut is an iterative algorithm that filters out the probe or sample with the highest fraction of unreliable measurements one at a time. Note that every iteration of the algorithm produces a matrix of retained measurements and a set of removed ones.

We calculated false positive rate (α) and sensitivity (s) when the retained measurements are considered as prediction for the reliable ones. Among all matrices produced by Greedycut, we selected the one that maximizes the value of the expression s + 1 - α, thereby giving equal weights to the sensitivity and specificity. Presented geometrically on a ROC curve, this is the point that is furthest from the diagonal. The results of the Greedycut procedure and the selected iteration are presented in the figure below.

Metric	table dimensions false positive rate sensitivity ROC curve distance to the diagonal in a ROC plot
Iterations to show	all

Figure 2

Change of table dimensions / metric related to accuracy as Greedycut progressively removes probes and samples. Accuracy is calculated by treating the retained entries as predictive of reliable measurements. The red circle, if present, marks the last iteration that was executed.

Based on the criteria described above, 757 probes and 0 samples were filtered out. Links to the lists of removed items are given below.

This section shows the influence of the applied normalization procedure on CpG methylation values. The following figure compares the distributions of the β values before and after performing normalization.

Plot type

density estimation histograms quantile-quantile plot

Figure 5

Comparison of β values before and after correction.Both distributions are estimated by randomly sampling 1000000 values in each group.

The next figure gives an idea of the magnitude of the correction by showing the distribution of shifts, i.e. degrees of modification of the raw methylation values.

Figure 6

Histogram of observed magnitude of β value correction.

The figure below gives a more detailed view. This color-coded 2D histogram shows the uncorrected β values and their respective shifts after performing the normalization procedure.

Figure 7

2D histogram showing the raw β values and the magnitude of the corrections.

The following figure visualizes the average methylation per sample. Samples are grouped by slide.

Figure 8

Point-and-whisker plot showing mean and standard deviation among all beta values in a sample.

In addition to CpG sites, there are 4 sets of genomic regions to be covered in the analysis. The table below gives a summary of these annotations.