RnBeads: Ewing Sarcoma

Option	Value
replicate.id.column	default
exploratory.columns	disease_type, disease_type_detailed, sample_source, tumor_location, tumor_size, relapse, metastases_at_diagnosis, comp.column, Tissue_vs_MSC
exploratory.top.dimensions	all
exploratory.principal.components	5
exploratory.correlation.pvalue.threshold	0.01
exploratory.correlation.permutations	10000
exploratory.correlation.qc	yes
exploratory.beta.distribution	yes
exploratory.intersample	yes
exploratory.deviation.plots	no
exploratory.clustering	top
exploratory.clustering.top.sites	1000
exploratory.clustering.heatmaps.pdf	no
distribution.subsample	1000000
exploratory.gene.symbols	CXCL8, EMILIN1, C20orf141
exploratory.custom.loci.bed	default

Trait	Number of groups
disease_type	2
disease_type_detailed	4
sample_source	4
tumor_location	7
tumor_size	2
relapse	2
metastases_at_diagnosis	2
comp.column	2
Tissue_vs_MSC	2

In addition to CpG sites, there are 6 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
tiling1kb	n.a.	425860
tiling200bp	n.a.	727157
genes	Ensembl genes, version Ensembl Genes 78	32091
promoters	Promoter regions of Ensembl genes, version Ensembl Genes 78	31924
cpgislands	CpG island track of the UCSC Genome browser	23260
ensembleRegBuildBPall	Ensembl Regulatory build from BLUEPRINT data release 20150820 -- all	112231

Region length distributions

The plots below show region size distributions for the region types above.

Region type

Figure 1

Distribution of region lengths

Number of sites per region

The plots below show the distributions of the number of sites per region type.

Region type

Figure 2

Distribution of the number of sites per region

Region site distributions

The plots below show distributions of sites across the different region types.

Region type

Figure 3

Distribution of sites across regions. relative coordinates of 0 and 1 corresponds to the start and end coordinates of that region respectively. Coordinates smaller than 0 and greater than 1 denote flanking regions normalized by region length.

Low-dimensional Representation

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).

One or more of the methylation matrices was augmented before applying the dimension reduction techniques because it contains missing values. 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
sites	MDS	2217786	0	2217786
sites	PCA	2217786	2164759	53027
tiling1kb	MDS	425860	0	425860
tiling1kb	PCA	425860	366682	59178
tiling200bp	MDS	727157	0	727157
tiling200bp	PCA	727157	683480	43677
genes	MDS	32091	0	32091
genes	PCA	32091	10149	21942
promoters	MDS	31924	0	31924
promoters	PCA	31924	16924	15000
cpgislands	MDS	23260	0	23260
cpgislands	PCA	23260	11951	11309
ensembleRegBuildBPall	MDS	112231	0	112231
ensembleRegBuildBPall	PCA	112231	81220	31011

Multidimensional Scaling

The scatter plot below visualizes the samples transformed into a two-dimensional space using MDS.

Location type
Distance
Sample representation
Sample color

Figure 4

Scatter plot showing samples after performing Kruskal's non-metric mutidimensional scaling.

Principal Component Analysis

Similarly, the figure below shows the values of selected principal components in a scatter plot.

Location type
Principal components
Sample representation
Sample color

Figure 5

Scatter plot showing the samples' coordinates on principal components.

The figure below shows the cumulative distribution functions of variance explained by the principal components.

Location type

Figure 6

Cumulative distribution function of percentange of variance explained.

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.

Location Type	Number of Components	Full Table File
sites	157	csv
tiling1kb	148	csv
tiling200bp	151	csv
genes	142	csv
promoters	142	csv
cpgislands	129	csv
ensembleRegBuildBPall	142	csv

Batch Effects

In this section, different properties of the dataset are tested for significant associations. The properties can include sample coordinates in the principal component space, phenotype traits and intensities of control probes. The tests used to calculate a p-value given two properties depend on the essence of the data:

If both properties contain categorical data (e.g. tissue type and sample processing date), the test of choice is a two-sided Fisher's exact test.
If both properties contain numerical data (e.g. coordinates in the first principal component and age of individual), the correlation coefficient between the traits is computed. A p-value is estimated using permutation tests with 10000 permutations.
If property A is categorical and property B contains numeric data, p-value for association is calculated by comparing the values of B for the different categories in A. The test of choice is a two-sided Wilcoxon rank sum test (when A defines two categories) or a Kruskal-Wallis one-way analysis of variance (when A separates the samples into three or more categories).

Note that the p-values presented in this report are not corrected for multiple testing.

Associations between Principal Components and Traits

The computed sample coordinates in the principal component space were tested for association with the specified traits. Below is a list of the traits and the tests performed.

Trait	Test
disease_type	Wilcoxon
disease_type_detailed	Kruskal-Wallis
sample_source	Kruskal-Wallis
tumor_location	Kruskal-Wallis
tumor_size	Wilcoxon
relapse	Wilcoxon
metastases_at_diagnosis	Wilcoxon
comp.column	Wilcoxon
Tissue_vs_MSC	Wilcoxon

The heatmap below summarizes the results of permutation tests performed for associations. Significant p-values (values less than 0.01) are displayed in pink background.

Region type

Figure 7

Heatmap presenting a table of p-values. Significant p-values (less than 0.01) are printed in pink boxes. Non-significant values are represented by blue boxes. Bright grey cells, if present, denote missing values.

The full tables of p-values for each location type are available in CSV (comma-separated value) files below.

Location Type	File Name
sites	csv
tiling1kb	csv
tiling200bp	csv
genes	csv
promoters	csv
cpgislands	csv
ensembleRegBuildBPall	csv

Associations between Traits

This section summarizes the associations between pairs of traits.

The figure below visualizes the tests that were performed on trait pairs based on the description provided above. In some cases, pairs of traits could not be tested for associations. These scenarios are marked by grey shapes, and the underlying reason is given in the figure legend. In addition, the calculated p-values for associations between traits are shown. Significant p-values (values less than 0.01) are displayed in pink background. The full table of p-values is available in a dedicated file that accompanies this report.

Heatmap of

Figure 8

(1) Table of performed tests on pairs of traits. Test names (Correlation + permutation test, Fisher's exact test, Wilcoxon rank sum test and/or Kruskal-Wallis one-way analysis of variance) are color-coded according to the legend given above.
(2) Table of resulting p-values from the performed tests on pairs of traits. Significant p-values (less than 0.01) are printed in pink boxes Non-significant values are represented by blue boxes. White cells, if present, denote missing values.

Methylation Value Distributions

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.

Methylation Value Densities of Sample Groups

The plots below compare the distributions of methylation values in different sample groups, as defined by the traits listed above.

Sample trait
Methylation of

Figure 9

Beta value density estimation according to sample grouping.

Methylation Value Densities of Site Categories

In a similar fashion, the plot below compares the distributions of beta values in different site types.

Sample group
Site category

Figure 10

Methylation value density estimation according to sample grouping and site category.

Inter-sample Variability

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

Figure 11

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

Figure 12

Scatter plot showing the correlation betweeen region mean methylation and the variance across a group of samples. Every point corresponds to one region.

Clustering

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

Figure 13

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

Figure 14

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

Figure 15

Probe and sample colors used in the heatmaps in the previous figures.

Identified Clusters

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

Figure 16

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)	151
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

Clusters and Traits

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

Figure 17

Heatmap visualizing Rand indices computed between sample traits (rows) and clustering algorithm outcomes (columns).

Exploratory Analysis

Parameter Overview

Sample Groups

Region Annotations

Region length distributions

Number of sites per region

Region site distributions

Low-dimensional Representation

Multidimensional Scaling

Principal Component Analysis

Batch Effects

Associations between Principal Components and Traits

Associations between Traits

Methylation Value Distributions

Methylation Value Densities of Sample Groups

Methylation Value Densities of Site Categories

Inter-sample Variability

Clustering

Identified Clusters

Clusters and Traits

Regional Methylation Profiles

Locus Profiles (Genes)