# Analyzing Illumina Infinium Methylation array data This guide provides instructions for analyzing Illumina Infinium Methylation array data. The tutorial uses this [Infinium Methylation Screening Array Demo Data Set](https://support.illumina.com/downloads/infinium-methylation-demo-data-set.html) if following along exactly with the analyses pipeline. If you are new to Partek Flow, please see the [Quick start guide](https://help.partek.illumina.com/partek-flow/quick-start-guide) for information about the Partek Flow user interface. ## Import Illumina methylation array data We recommend uploading the microarray data to a folder on your Partek Flow server before importing into a project. Data files can be transferred to your server from the *Home* *page* by clicking the **Transfer file** button. Users have the option to change the *Upload directory* by clicking the **Browse** button and either select another existing directory or create a new directory. [Please click here for more information on transferring files to the server](https://help.partek.illumina.com/partek-flow/user-manual/importing-data#navigating-the-file-browser-to-transfer-files-to-the-server). To create a new project on the *Home page* click the +**Add data** button, enter a project name, and click **Create project**.

*Give the project a name then click Create project*

Upon project creation you will land in the Analyses tab, prompting the addition of sample data to the project. Click the blue **Add data** button

When available, hover over Tooltip

Tooltips or click the Video icon

video help for decision making.

*Add data to the project by clicking Add data (blue circle)*

Select **Microarray**, **Methylation** and **Illumina methylation idat** as the file format for import then click **Next**.

*Choose Microarray, Methylation, and Illumina methylation idat then click Next*

Navigate to the idat files that have been uploaded to the server. For this tutorial, there are two paired idat files per sample. If you have not already transferred the files to the server you can choose to do this within the import task by clicking the **Transfer files to the server** button.

Transfer idat files to the server by clicking Transfer files to the server

This will bring you to the Transfer files page. Click the **Transfer files** button, add the files for transfer then click **Upload**. Do not terminate the browser or let your computer go to sleep during transfer. A time estimate for upload is provided but may change.

*Transfer the files to the server where you can find them*

When the transfer completes the upload window will close. In the *Transfer files* page, the transferred files *Status* is *Complete*.

The Transfer files page shows the idat file transfer is complete

Now, the selected files are on the server in the folder specified during transfer. In the project import task, navigate to the files on your server. In the example below, only these files were saved to this folder so I will check the top box to select all then click **Finish**.

*Select the paired idat sample files for import into the project*

This starts the *Importing* of selected data to the project. The transparent task bar will complete as import progresses.

When the import completes, the *Microarray methylation* data node appears in the *Analyses* tab. Hovering of this node, we see 60 samples (572.28 MB data) are contained in this data node.

*The Microarray methylation data node contains the imported data, hover over the node to see details*

## Assign sample metadata Add sample metadata to the project by navigating to the *Metadata* tab. Select the **Assign values from file** button as an efficient way to assign sample attributes using a tab delimited text file.

*Add sample metadata in the Metadata tab*

If the samples metadata file is not already on the server, click **Transfer files to the server**.

Select the file then click **Next**. The tab delimited file should contain a table with the following: * The first column of the table lists the sample names (the sample names in the file must be identical to the ones listed in the project *Sample name* column in the *Metadata* tab) * The first row lists the attribute names (e.g. Treatment, Exposure) * List any corresponding attributes for each sample in succeeding columns Make any wanted modifications and click **Import**.

This adds the defined attribute information to the *Metadata* tab. **Manage** and **Assign values** buttons can be used to further modify sample attributes.

*Manage and Assign values in the Metadata tab to further modify sample attributes*

Click the left **Analyses** tab to navigate back to the analyses pipeline. Single-click the **Microarray methylation** data node to run the first task using the context sensitive menu on the right. No tasks have been performed on this data so there is still an option to *Add data* to the project; once analysis tasks are performed from this data node, data can no longer be added to the project.

*Run the first task on the Microarray methylation data node using the context sensitive menu on the right*

## Generate beta value Using the task menu on the right under *Methylation analysis* select the **Generate beta value** task. Choose the Chip name as *Infinium Methylation Screening Array*, If the Chip name is not listed use the dropdown to select **New Chip** then add the Illumina manifest file. Keep the default settings and click **Finish**.

*Choose the appropriate manifest file (Chip name), keep the default settings then click Finish*

This task output is the *Methylation beta* data node. Methylation Beta-values are continuous variables between 0 and 1. Single-click the *Methylation beta* data node node and choose the next **PCA** task under *Exploratory analysis* from the task menu.

## Generate PCA In the *Analyses* tab, select the *Methylation beta* data node then use the task menu *Exploratory analyses* options to run the **PCA** task *.* Keep the default settings the same and click **Finish**.

*Que the PCA task with default settings and click Finish*

In the *Analyses* tab, double click the *PCA* data node (circle) or single click the data node and select **Task report** under *Task results* in the task menu to view the PCA results in the Data Viewer.

## Detect differential methylation Single-click the *Methylation beta* data node node and perform the **Detect differential methylation** task under *Methylation analysis* in the task menu.

*Select the Methylation beta data node then que the Detect differential methylation task*

This task converts the Beta-values to M-values and uses these to perform ANOVA differential expression analysis. [Please click here for more information on the ANOVA model.](https://help.partek.illumina.com/partek-flow/user-manual/task-menu/differential-analysis/anova-limma-trend-limma-voom) 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.

*Open the Detect differential methylation report*

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. Click the Optional columns button to add more column data including annotation from the Illumina manifest file. For more information on these optional columns [please see the "Infinium Methylation Screening Array Manifest Column Headings pdf here.](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.

*Use the filter panel to filter the results then click Generate filtered node*

The filtered node is now available in the *Analyses* pipeline. ## Visualize filtered results with Hierarchical clustering / heatmap Select the generated *Filtered feature list* data node and use the *Exploratory analysis* task menu dropdown to perform the **Hierarchical clustering / heatmap** task.

In the *Hierarchical clustering / heatmap* task settings, change *Sample order* to **Assign order**, select the attribute, then click **Finish**. This will order the heatmap rows based on the attribute order assigned.

*Modify the heatmap task settings and click Finish*

[Click here for more information on Hierarchical clustering task settings to visualize a Heatmap or Bubble map](https://help.partek.illumina.com/partek-flow/user-manual/task-menu/exploratory-analysis/hierarchical-clustering#invoking-hierarchical-clustering) Completion of this task will output the *Hierarchical clustering / heatmap* task results. Double-click this node or select **Task report** under *Task results* from the task menu to open the results in the Data viewer.

*Open the Hierarchical clustering / heatmap task results*

The heatmap visualization can be altered within the Data viewer using both the left menu and the in-plot controls.

*Modify the task results using the Data viewer controls*

[Please click here for more information on using the Data viewer to modify visualizations.](https://help.partek.illumina.com/partek-flow/user-manual/data-viewer) Click **Save as** in the left menu to save the data viewer session to return and make changes. To save the full individual image within the Data viewer to your machine, click the in-plot

**Export image** icon in the top right corner of the image, choose **All data** then select the format, size, and resolution and click **Save**.

*Save the heatmap to your machine by selecting All data*

### Perform biological interpretation Select the *Filtered feature list* data node within the Analyses tab and choose the **Gene set enrichment** task under *Biological interpretation* in the task menu.

*Run the Gene set enrichment task on the filtered feature list data node*

Use the *Gene set enrichment* task settings to change default parameters. Choose the KEGG database. Click the dropdown to change the selection; select **New library to** add the most recent library available. Check **Select feature identifier** as *Gene Symbol* or *Gene IDs* to ensure genes are used instead of probe IDs for this task. After optimizing the task settings, click Finish.

*Optimize the settings for the Gene set enrichment task*

The output of the *Gene set enrichment* task node is the *Pathway enrichment* data node. ### Filter the Gene set enrichment result Filter the KEGG pathway gene sets by selecting the Pathway enrichment data node then clicking the **Filter gene sets** task from the task menu.

*Select the Pathway enrichment and click the Filter gene sets task*

Modify the Filter by parameters to include P-value < 0.050 then click Finish.

*Modify the Filter by parameters to include P-value < 0.050 and click Finish*

Completion of this task will output a *Filter gene sets* task node and a *Filtered list* data node in the Analyses tab. Open the filtered gene sets by selecting the *Filtered list* data node and clicking the task menu **Task report** from the right task menu.

*Open the filtered gene sets by double clicking on the Filtered list data node*

Because we filtered the gene sets to fewer than 100 rows, click the button to **View plots in the Data Viewer**. The filtering step can also be performed within the Pathway enrichment report.

*Filtering to fewer than 100 rows allows the View plots in the Data Viewer button*

This opens the filtered list report in the Data viewer for further modification.

*Filtered list of KEGG pathway results in the Data viewer*

[Please click here for more information on using the Data viewer to modify visualizations.](https://help.partek.illumina.com/partek-flow/user-manual/data-viewer) Save this data viewer session, with a meaningful name to revisit for future analysis, using the **Save as** button in the left menu. This saved session is accessible by selecting the project **Data viewer** tab or by clicking *Data Viewer* within the breadcrumb (shown above the Data viewer canvas). [Please click here for more information on the Gene set enrichment](https://help.partek.illumina.com/partek-flow/user-manual/task-menu/biological-interpretation/gene-set-enrichment) task like the interactive KEGG pathway maps. ## Analyses pipeline for Infinium methylation array data analysis This completes the example analyses pipeline.