Detecting Differentially Methylated Loci

To detect differential methylation between CpG loci in different experimental groups, we can perform an ANOVA test.

Select Defect Differential Methylation from the Analysis section of the Illumina BeadArray Methylation workflow
Select 2. State from the Experimental Factor(s) panel of the ANOVA of Spreadsheet 1 dialog
Select Add Factor > to move 2. State to the ANOVA Factor(s) panel
Repeat for 3. shRNA treatment
Select 2. State and 3. shRNA treatment from the Experimental Factor(s) panel by holding the Ctrl key on your keyboard and selecting both
Select Add Interaction > to add the interaction between 2. State and 3. shRNA treatment
Select Contrasts...

go to Analysis > Detect Differential Methylation. In the ANOVA dialog (Figure 1), select Add Factor to move the factor 2. HPSC from Experimental Factor(s) to the ANOVA Factor(s) box.

Figure 1. ANOVA setup dialog. Experimental factors listed on the left can be added to the ANOVA model. To set up an interaction, select two factors in the Experimental Factor(s) list. ANOVA models can be saved and resued using Save Model... and Load Model... buttons. Contrasts... allow for setting up group comparisons. Cross Tabs launches a web browser window with breakdown of samples across the factors. Advanced... enables fine tuning of the algorithm and the output

Depending on the data in the top level (i.e. parent) spreadsheet you may need to manually specify whether the data is already log transformed: you should select Yes for M-values, as they are based on logit transformation (Figure 2). By default, Partek Genomics Suite will calculate fold-change value for each contrast and, in addition to that, if you want to include the difference in methylation levels between the groups at each CpG site in the output, check the Estimate box in the Other Statistics section of the dialog (Figure 2). The setup of the contrasts has implications on downstream steps, in particular on filtering the differentially methylated loci.

Figure 2. Setting ANOVA contrasts. For an accurate fold-change calculation it is essential to specify if the data has already been log transformed. Fold change is reported as Group 1 over Group 2. The Estimate (Other Statistics) is the difference between Group 1 and Group 2 (un-checked by default)

For this exercise, we shall compare primed HPSC with suppression of OCT4 (shPOU5F1) and primed HPSC with suppression of NANOG (shNANOG) with the baseline primed cells. To start with, select the Primed group in the Candidate Level(s) box and push Add Contrast Level > to move the Primed group to Group 2 (lower box). Then Ctrl & select both shPOU5F1 and shNANOG in the Candidate Level(s) box and push Add Contrast Level > to move them to Group 1 (upper box). Then click Add Combinations and confirm that two contrast have been created as seen in Figure 3.

Figure 3. Section of the contrasts setup dialog with an example of two contrasts

Push OK to confirm the contrast (and close the contrast dialog) and again to start the ANOVA calculation.

The first time you use MethylationEPIC array, the manifest file needs to be configured and the window like the one in Figure 4 will pop up. First select the second option (Chromosome is in one column and the physical location is in another column). Marker ID is on the first column (Ilmn ID), Chromosome is on the column CHR, while the Physical Position is on the MAPINFO column. Set according to Figure 4 and select Close. This enable Partek Genomics Suite to parse out probe annotation from the manifest file.

Figure 4. 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 result of 1-way ANOVA is shown in Figure 5. Each row of the table represents a single CpG locus (identified by Probeset ID column). The remaining columns contain the following information:

Column 3. Gene Symbol: the gene overlapping the probe as specified in the Illumina manifest file

Column 4. p-value(HPSC): overall p-value for the specified factor (in parenthesis). A low p-value indicates that there is a difference in methylation between the levels of this attribute (i.e. study groups). The contrast p-values should then be used to evaluate individual group comparisons. If more than one factor is included in the model, p-value will be reported for each.

Next, for each contrast included in the model, a block of seven columns will be added, as follows:

Column 5. p-value(shNANOG vs. Primed): p-value for the given contrast (in parenthesis). A low p-value indicates a difference in methylation between the groups included in the contrast (here: shNANOG and Primed).

Column 6. Ratio(shNANOG vs. Primed): ratio of average methylation level in one over the other the other contrasted group (shNANOG and Primed, respectively). Ratio is reported in linear space.

Column 7. Fold Change(shNANOG vs. Primed): fold-change in one over the other contrasted group (shNANOG and Primed, respectively). Fold-change is reported in linear space.

Column 8. Fold Change(shNANOG vs. Primed) (Description): if fold-change > 1, it means hypermethylation in the first group (e.g. shNANOG up vs Primed), if fold-change < -1, it means hypomethylation in the first group (e.g. shNANOG down vs Primed), relative to the second group (Primed). This column enables quick filtering

Columns 9. & 10. Lower and upper (respectively) limits of 95% confidence interval of the fold-change

Column 11. Estimate(shNANOG vs. Primed): difference between means of two groups (i.e. shNANOG and Primed) (this column is optional and depends on the way contrasts were set up)

Columns 12. - 18. correspond to columns 5. - 11.

Columns 19.+ Statistical output

Figure 5. ANOVA spreadsheet (truncated). Each row is a result of an ANOVA at a given CpG locus (identified by the Probeset ID columns). The remaining columns contain annotation and statistical output

Going forward, analysis of differentially methylated loci typically includes removal of the probes on X and Y chromosomes (to avoid the problems with inactivation of one X chromosome). To annotate the ANOVA spreadsheet with the information required for filtering, right-click on the Gene Symbol column, select Insert Annotation, tick-mark the CHR filed (Figure 6) and push OK. A new column will be appended to the spreadsheet.

Figure 6. Adding annotation to the spreadsheet. The dialog provides access to the Illumina manifest file

To enable filtering right-click on the header of the CHR column > Properties and set the type to categorical (and OK). Now, activate the Interactive Filter tool () . If needed use the drop-down list to point to the CHR column. The column chart represents the number of appearances of each chromosome in the spreadsheet (i.e. the number of probes per chromosome). To remove the probes on the X and the Y chromosome left click on the two right-most columns (the pop up balloon will show you the chromosome label) and the columns will be grayed out (Figure 7).

Figure 7. Using Interactive Filter tool to filter out probes by annotation. When pointed to a categorical column, the Interactive Filter tool summarises the content of the column by a column chart. Left-click to exclude a category (two columns on the right were excluded, so they are grayed out), right-click to include only

After removal of the probes on the sex chromosomes, let us extract all the autosomal probes to a new spreadsheet. Right click on the ANOVA 1-way spreadsheet > Clone... Set the Name of new spreadsheet to AutosomalOnly and make it a child of the top-level spreadsheet (EPIC iDAT) (Figure 8). Push OK. The newly created autosomalonly spreadsheet will be a new starting point for all the downstream steps.

Figure 8. Creating a clone of a spreadsheet as a way of extracting features in a separate spreadsheet. The clone should have the same parent as the original (i.e. template) spreadsheet

To come up with a list of differentially methylated loci, proceed to the workflow and select Create Marker List. That will open the List Manager functionality and you will be on the ANOVA Streamlined tab. Factors in the model are listed in the top section, contrasts specified in the model are in the middle, while the filter settings are at the bottom.

Select shNANOG vs Primed contrast to see the current filter configuration. By default, both fold-change and p-value with false discovery rate (FDR) correction are applied, with the number of CpG loci passing the filter given as # Pass. For this tutorial, set the fold-change to > 10 and < -10, and reduce the p-value with FDR down to 0.01 (Figure 9). Then push the Create button to save the list of significant loci under the default name (shNANOG vs. Primed). Repeat the procedure for the shPOU5F1 vs Primed, using the same cut offs.

Figure 9. Creating list of significant CpG loci using List Manager and the ANOVA Streamlined tab. You can specify either a factor or a contrast to work with. The filters are given at the bottom and can be configured as preferred. The Create button generates a new spreadsheet with significant CpG loci (the loci passing the filter)

The List Manager can now be closed by selecting the Close button.

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