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Once the .idat files are imported, you will be facing the Scatter Plot. To move forward with the tutorial, switch to the Analysis tab and take a look at the top level (i.e. initial) Each row of the spreadsheet (Figure 1) . Samples identifiers are corresponds to a single sample. The first column is the names of the .idat files as shown by the Sample ID column while all the and the remaining columns are the array probes.

 

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SubtitleTextSpreadsheet after .idat file import: samples on rows (Sample IDs are based on file names), probes on columns, cell values are functionally normalized beta values (default settings)
AnchorNametop level spreadsheet

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As discussed in the previous chapter, the values in the cells are normalized  The table values are β-values,  which which correspond to the percentage of methylation at each site and are calculated as . A β-value is calculated as the ratio of methylated probe intensity over the overall intensity at each site (the overall intensity is the sum of methylated and unmethylated probe intensities). An alternative metric for measurement of methlyation levels are M-values. β-values can be easily converted to M-values using the following equation:

M-value = log2( β / (1 - β))

Here's the interpretation: a M-value close to 0 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.

Therefore, the differential methylation analysis of the tutorial data will be performed on the M-values; select Convert Beta Value to M Value from the workflow. Note that the original data (β-values) will be overwritten.

Before proceeding to exploratory data

 

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SubtitleTextSpreadsheet after .idat file import: samples on rows (Sample IDs are based on file names), probes on columns, cell values are functionally normalized beta values (default settings)
AnchorNametop level spreadsheet

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Before we can perform any analysis, the study samples need to be organized in four groups, two biological replicates each. In the Import section of the workflow, select Add Sample Attributes and, in the next into their experimental groups.

  • Select Add Sample Attributes from the Import section of the Illumina BeadArray Methylation workflow
  • Select Add a Categorical Attribute from the Add Sample Attributes dialog (Figure 2)

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SubtitleTextAdding sample attributes. Adding Attributes from an Existing Column can be used to split file names into sections, based on delimiters (e.g. _, -, space etc.). Adding a Numeric or Categorical Attribute enables the user to manually specify sample attributes
AnchorNameadd sample attributes selector

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  • Select OK

The Create categorical attribute dialog allows us to create groups for a categorical attribute. By default, two groups are created, but additional groups can be added. 

  • Set Attribute name: to Cell Type
  • Rename the groups B cells and LCLs
  • Drag and drop the samples from the Unassigned list to their groups as listed in the table below
Sample IDCell Type
GSM2452106_200483200025_R04C01B cells
GSM2452107_200483200021_R01C01B cells
GSM2452108_200483200021_R02C01B cells
GSM2452109_200483200025_R06C01B cells
GSM2452110_200483200025_R07C01B cells
GSM2452111_200483200021_R08C01B cells
GSM2452112_200483200021_R06C01B cells
GSM2452113_200483200021_R04C01B cells
GSM2452114_200483200025_R01C01LCLs
GSM2452115_200483200025_R03C01LCLs
GSM2452116_200483200021_R03C01LCLs
GSM2452117_200483200025_R05C01LCLs
GSM2452118_200483200025_R02C01LCLs
GSM2452119_200483200021_R07C01LCLs
GSM2452120_200483200021_R05C01LCLs
GSM2452121_200483200025_R08C01LCLs

There should now be two groups with eight samples in each group (Figure 3).

 

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SubtitleTextAdding Cell Type attribute as a categorical group
AnchorNameState attribute

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  • Select OK
  • Select Yes from the Add another categorical attribute dialog
  • Set Attribute name: to Gender
  • Rename the groups Male and Female
  • Drag and drop the samples from the Unassigned list to their groups as listed in the table below
Sample IDGender
GSM2452106_200483200025_R04C01Female
GSM2452107_200483200021_R01C01Female
GSM2452108_200483200021_R02C01Male
GSM2452109_200483200025_R06C01Female
GSM2452110_200483200025_R07C01Female
GSM2452111_200483200021_R08C01Female
GSM2452112_200483200021_R06C01Female
GSM2452113_200483200021_R04C01Male
GSM2452114_200483200025_R01C01Female
GSM2452115_200483200025_R03C01Female
GSM2452116_200483200021_R03C01Male
GSM2452117_200483200025_R05C01Female
GSM2452118_200483200025_R02C01Female
GSM2452119_200483200021_R07C01Female
GSM2452120_200483200021_R05C01Female
GSM2452121_200483200025_R08C01Male

There should now be two groups with four samples in Male and twelve samples in Female (Figure 4).

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SubtitleTextAdding Gender attribute as a categorical group
AnchorNameState attribute

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  • Select OK
  • Select No from the Add another categorical attribute dialog
  • Select Yes to save the spreadsheet

Two new columns have been added to spreadsheet 1 (Methylation) with the cell type and gender of each sample (Figure 5). 

 

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SubtitleTextAnnotated beta values spreadsheet
AnchorNameAnnotated spreadsheet

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