Detect differentially methylated loci
Last updated
Last updated
To detect differential methylation between CpG loci in different experimental groups, we can perform an ANOVA test. For this tutorial, we will perform a simple two-way ANOVA to compare the methylation states of the two experimental groups.
Select Detect Differential Methylation from the Analysis section of the Illumina BeadArray Methylation workflow
A new child spreadsheet, mvalue, is created when Detect Differential Methylation is selected. M-values are an alternative metric for measuring methylation. β-values can be easily converted to M-values using the following equation: M-value = log2( β / (1 - β)).
An M-value close to 0 for a CpG site indicates a similar intensity between the methylated and unmethylated probes, which means the CpG site is about half-methylated. Positive M-values mean that more molecules are methylated than unmethylated, while negative M-values mean that more molecules are unmethylated than methylated. As discussed by Du and colleagues, the β-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels.
Because we are performing differential methylation analysis, Partek Genomics Suite automatically creates an M-values spreadsheet to use for statistical analysis.
Select 2. Cell Type and 3. Gender from the Experimental Factor(s) panel
Select Add Factor > to move 2. Cell Type and 3. Gender to the ANOVA Factor(s) panel (Figure 1)
Figure 1. ANOVA setup dialog. Experimental factors listed on the left can be added to the ANOVA model.
Select Contrasts...
Leave Data is already log transformed? set to No
Leave Report comparisons as set to Difference
For methylation data, fold-change comparisons are not appropriate. Instead, comparisons should be reported as the difference between groups.
Select 2. Cell Type from the Select Factor/Interaction drop-down menu
Select LCLs
Select Add Contrast Level > for the upper group
Select B cells
Select Add Contrast Level > for the lower group
Select Add Contrast (Figure 2)
Figure 2. Configuring ANOVA contrasts
Select OK to close the Configuration dialog
The Contrasts... button of the ANOVA dialog now reads Contrasts Included
Select OK to close the ANOVA dialog and run the ANOVA
If this is the first time you have analyzed a MethylationEPIC array using the Partek Genomics Suite software, the manifest file may need to be configured. If it needs configuration, the Configure Annotation dialog will appear (Figure 3).
Select Chromosome is in one column and the physical location is in another column for Choose the column configuration
Select Ilmn ID for Marker ID
Select CHR for Chromosome i
Select MAPINFO for Physical Position
Select Close
This enables Partek Genomics Suite to parse out probe annotations from the manifest file.
Figure 3. Processing the annotation file. User needs to point to the columns of the annotation file that contain the probe identifier as well as the chromosome and coordinates of the probe.
The results will appear as ANOVA-2way (ANOVAResults), a child spreadsheet of mvalue. Each row of the spreadsheet represents a single CpG locus (identified by Column ID).
Figure 4. ANOVA spreadsheet. Each row is a result of an ANOVA at a given CpG locus (identified by the Column ID column). The remaining columns contain annotation and statistical output
For each contrast, a p-value, Difference, Difference (Description), Beta Difference, and Beta Difference (Description) are generated. The Difference column reports the difference in M-values between the two groups while the Beta Difference column reports the difference in beta values between the two groups.
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