The specified traits were tested based on criteria for defining sample groups. The table below summarizes these traits.
| Trait | Number of groups |
| Cell_type | 2 |
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.
| Annotation | Description | Regions in the Dataset |
| tiling | n.a. | 568648 |
| genes | n.a. | 52647 |
| promoters | n.a. | 56639 |
| cpgislands | n.a. | 27257 |
The plots below show region size distributions for the region types above.
The plots below show the distributions of the number of sites per region type.
Sample replicates were compared. This section shows pairwise scatterplots for each sample replicate group on both site and region level.
| replicate | |
| site/region |
Scatterplot for replicate methylation comparison. The transparency corresponds to point density. The 1% of the points in the sparsest populated plot regions are drawn explicitly.
The following table contains pearson correlation coefficients:
| sites | tiling | genes | promoters | cpgislands | |
| Human.EWS_FLI1_high.NA.1 vs. Human.EWS_FLI1_high.NA.2 (EWS_FLI1_high) | 0.8261 | 0.8543 | 0.8925 | 0.9559 | 0.987 |
| Human.EWS_FLI1_low.NA.1 vs. Human.EWS_FLI1_low.NA.2 (EWS_FLI1_low) | 0.8074 | 0.8496 | 0.8854 | 0.9546 | 0.9857 |
Dimension reduction is used to visually inspect the dataset for a strong signal in the methylation values that is related to samples' clinical or batch processing annotation. RnBeads implements two methods for dimension reduction - principal component analysis (PCA) and multidimensional scaling (MDS).
The analyses in the following sections are based on selected sites and/or regions with highest variability in methylation across all samples. The following table shows the maximum dimensionality and the selected dimensions in each setting (column names Dimensions and Selected, respectively). The column Missing lists the number of dimensions ignored due to missing values. In the case of MDS, dimensions are ignored only if they contain missing values for all samples. In contrast, sites or regions with missing values in any sample are ignored prior to PCA.
| Sites/regions | Technique | Dimensions | Missing | Selected | Variance explained |
| sites | MDS | 24962867 | 0 | 20000 | NA |
| sites | PCA | 24962867 | 11236574 | 20000 | 4.2 |
| tiling | MDS | 568648 | 0 | 20000 | NA |
| tiling | PCA | 568648 | 3350 | 20000 | 45.9 |
| genes | MDS | 52647 | 0 | 20000 | NA |
| genes | PCA | 52647 | 3329 | 20000 | 97.9 |
| promoters | MDS | 56639 | 0 | 20000 | NA |
| promoters | PCA | 56639 | 975 | 20000 | 94.0 |
| cpgislands | MDS | 27257 | 0 | 20000 | NA |
| cpgislands | PCA | 27257 | 486 | 20000 | 99.4 |
The scatter plot below visualizes the samples transformed into a two-dimensional space using MDS.
| Location type | |
| Distance | |
| Sample representation | |
| Sample color |
Scatter plot showing samples after performing Kruskal's non-metric mutidimensional scaling.
Similarly, the figure below shows the values of selected principal components in a scatter plot.
| Location type | |
| Principal components | |
| Sample representation | |
| Sample color |
Scatter plot showing the samples' coordinates on principal components.
The figure below shows the cumulative distribution functions of variance explained by the principal components.
The table below gives for each location type a number of principal components that explain at least 95 percent of the total variance. The full tables of variances explained by all components are available in comma-separated values files accompanying this report.
Batch effects were not studied because none of the traits can be tested for association with sample coordinates in the principal components space.
Methylation value distributions were assessed based on selected sample groups. This was done on site and region levels. This section contains the generated density plots.
The plots below compare the distributions of methylation values in different sample groups, as defined by the traits listed above.
The variability of the methylation values is measured in two aspects: (1) intra-sample variance, that is, differences of methylation between genomic locations/regions within the same sample, and (2) inter-sample variance, i.e. variability in the methylation degree at a specific locus/region across a group of samples.
The following figure shows the relationship between average methylation and methylation variability of a site.
| Sample group | |
| Point color based on |
Scatter plot showing the correlation betweeen site mean methylation and the variance across a group of samples. Every point corresponds to one site.
In a complete analogy to the plots above, the figure below shows the relationship between average methylation and methylation variability of a genomic region.
| Regions | |
| Sample group | |
| Point color based on |
Scatter plot showing the correlation betweeen region mean methylation and the variance across a group of samples. Every point corresponds to one region.
The figure below shows clustering of samples using several algorithms and distance metrics.
| Site/region level | |
| Dissimilarity metric | |
| Agglomeration strategy (linkage) | |
| Sample color based on |
Hierarchical clustering of samples based on 1000 most variable loci. The heatmap displays methylation percentiles per sample. The legend for sample coloring can be found in the figure below.
| Site/region level | |
| Dissimilarity metric | |
| Agglomeration strategy (linkage) | |
| Sample color based on | |
| Site/region color based on | |
| Visualize |
Hierarchical clustering of samples based on 1000 most variable loci. The heatmap displays only selected sites/regions with the highest variance across all samples. The legend for locus and sample coloring can be found in the figure below.
| Site/region level | |
| Sample color based on | |
| Site/region color based on |
Probe and sample colors used in the heatmaps in the previous figures.
Using the average silhouette value as a measure of cluster assignment [1], it is possible to infer the number of clusters produced by each of the studied methods. The figure below shows the corresponding mean silhouette value for every observed separation into clusters.
| Site/region level | |
| Dissimilarity metric |
Line plot visualizing mean silhouette values of the clustering algorithm outcomes for each applicable value of K (number of clusters).
The table below summarizes the number of clusters identified by the algorithms.
| Site/region level |
| Metric | Algorithm | Clusters |
| correlation-based | hierarchical (average linkage) | 2 |
| correlation-based | hierarchical (complete linkage) | 2 |
| correlation-based | hierarchical (median linkage) | 2 |
| Manhattan distance | hierarchical (average linkage) | 2 |
| Manhattan distance | hierarchical (complete linkage) | 2 |
| Manhattan distance | hierarchical (median linkage) | 2 |
| Euclidean distance | hierarchical (average linkage) | 2 |
| Euclidean distance | hierarchical (complete linkage) | 2 |
| Euclidean distance | hierarchical (median linkage) | 2 |
The figure below shows associations between clusterings and the examined traits. Associations are quantified using the adjusted Rand index [2]. Rand indices near 1 indicate high agreement while values close to -1 indicate seperation. The full table of all computed indices is stored in the following comma separated files:
| Site/region level | |
| Dissimilarity metric |
Heatmap visualizing Rand indices computed between sample traits (rows) and clustering algorithm outcomes (columns).
Methylation profiles were computed for the specified region types. Composite plots are shown. Each individual region was subdivided into bins of equal sizes according to the following table:
| #bins in region | #extension bins | |
| genes | 6 | 2 |
| promoters | 6 | 2 |
| cpgislands | 6 | 2 |
#bins denotes the number of bins a region has been divided into. #extension bins indicates the number of bins that have been prepended and appended to a region
| Region type | |
| Sample trait |
Regional methylation profiles (composite plots) according to sample groups. Each region in the corresponding region type has been subdevided into equally sized bins. Accross the methylation values the bins of all regions, scatterplot smoothers for each sample and sample group were fit. Horizontal lines indicate region boundaries. For smoothing, generalized additive models with cubic spine smoothing were used. Deviation bands indicate 95% confidence intervals
