# Methylation Array

This guide provides instructions for analyzing Illumina Infinium Methylation array data. The data set is [Infinium Methylation Screening Array Demo Data Set](https://support.illumina.com/downloads/infinium-methylation-demo-data-set.html). There are 60 human samples with two different groups and two dose levels in each group.

The input data for methylation array is two .idat files per sample:

* `<Sample name>_Grn.idat`
* `<Sample name>_Red.idat`

### Create analysis

After[ login to Connected Multiomics](https://help.multiomics.illumina.com/icm/introduction/#log-in-to-connected-multiomics), [create study and add the IDAT files to the study](https://help.connected.illumina.com/icm/studies/create-study) to create 60 samples, add [sample meta data](https://help.connected.illumina.com/icm/studies/view-studies/sample-metadata).

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Click New Analysis button (<img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-315d1d86f14027f22d3e77bb3e8caced9b1789a7%2Fimage%20(486).png?alt=media" alt="" data-size="line">), specify an analysis name, choose ***Custom: Illumina Infinium Methylation*** from *Analysis Type* drop-down list, and use **All Illumina Infinium Methylation samples**, click **Run Analysis.**

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A Microarray methylation data node is generated in the Analysis. Click on the data node and choose [**Generate beta value task**](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/pre-analysis-tools/generate-beta-value)**.**

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### Generate beta value

Choose **Infinium Methylation Screening Array** as the *Chip name* from the drop-down list. Users have the options to exclude probes on XY chromosomes depends on their study goal. To be more conservative, users can also exclude probes if their detect p-values are higher than a cutoff value in certain number of samples. Filter probes will remove noise and speed up the downstream analysis.

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After configure the dialog, click **Finish** to run the task.

### Generate PCA

Click on Methylation beta data node, choose [**PCA**](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/exploratory-analysis/pca) in the *Exploratory analysis* section on the menu.

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Keep the default settings and click **Finish**.

Double click on the PCA data node to open the report in Data viewer.

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### Detect differential methylation

Single click the *Methylation beta* data node and perform the [**Detect differential methylation**](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/detect-differential-methylation) task under *Statistics* in the task menu.

This task converts the beta values to m-values and performs [ANOVA](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis/anova-limma-trend-limma-voom) differential expression analysis on both beta value and m-value matrixes.

Follow along with the task to make one-way or two-way ANOVA comparisons. The configured ANOVA model is performed on both beta value and m-value matrices. Click **Finish**.

This outputs the *Detect differential methylation* task report list. Open the **Task report** from the task menu or double-click the data node.

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The outputs of this task include significance as *P-value* and *FDR step up* which is from the M-values. The *LSMeans* of the groups and the *Difference* are of the Beta-values.

When the difference is greater than or equal to 0.2, it site is labeled as **Hypermethylated** in *Methylation* field; if the difference is less than or equal to -0.2, it site is labeled as **Hypomethylated** in *Methylation* field. If the difference is between -0.2 to 0.2, it is labeled "**?**" in *Methylation* field.<br>

Click the Optional columns button to add more column data including annotation from the Illumina manifest file. For more information on these optional columns, see *Infinium Methylation Screening Array Manifest Column Headings* on [Illumina support site](https://support.illumina.com/downloads/infinium-methylation-screening-manifest-files.html).

Use the left filter panel to filter the results then click **Generate filtered node**. Detailed information on how to use the filter panel can be found [here](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/statistics/differential-analysis/gsa).

### Visualize filtered results with Hierarchical clustering / heatmap

If we use FDR <=0.05 and difference is greater that |0.7| on Cell Line vs Coriell as an example filter criteria to generate a filtered result.

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Select the generated *Filtered feature list* data node and use the *Exploratory analysis* task menu dropdown to perform the [**Hierarchical clustering / heatmap**](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/exploratory-analysis/hierarchical-clustering) task.

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Leave the settings in the dialog as default and click **Finish**.

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Double click on the Hierarchical clustering/heatmap to open the report.

### Biological interpretation

Select the *Filtered feature list* data node within the Analyses tab and choose the [**Gene set enrichment**](https://help.connected.illumina.com/icm/analyses/analysis-functionality/task-menu/biological-interpretation/gene-set-enrichment) task under *Biological interpretation* in the task menu. Choose the KEGG database, with other settings as default, click **Finish**. Double click on the output *Pathway enrichment* data node to open the report.

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When the table is filtered down to less than 100 row, the report can be viewed in Data viewer. The filter can be performed on the report table, or click on the *Pathway enrichment* data node, perform **Filter gene sets** in *Filtering* section. Click on <img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-a2c9d858a75743b7f791e43aba82b12970529c98%2Fimage%20(474).png?alt=media" alt="" data-size="line"> when it is available to open the plots.

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This completes the example analyses pipeline.

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