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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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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))

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, we need to convert our data to from β-values to M-values.

  • Select Convert Beta Value to M Value from the Import section of the Illumina BeadArray Methylation workflow

The original data (β-values) will be overwritten.

  • Select (Image Removed) from the icon bar to save the current spreadsheet 

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Before we can perform any analysis, the study samples need to be organized into their experimental groups.

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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 StateCell Type
  • Rename the groups Primed and Naivegroups B cells and LCLs
  • Drag and drop the samples from the Unassigned list to their groups as listed in the table below
Sample IDGroup NameCell Type
GSM2515899GSM2452106_200526580002_200483200025_R04C01B cells
GSM2452107_200483200021_R01C01PrimedB cells
GSM2515900GSM2452108_200526580002200483200021_R02C01PrimedB cells
GSM2515901GSM2452109_200526580002200483200025_R03C01R06C01NaiveB cells
GSM2515902GSM2452110_200526580002_R04C01Naive
GSM2515903_200526580002_R05C01Naive
GSM2515904_200526580002_R06C01Naive
GSM2515905_200526580002_R07C01Naive
GSM2515906_200526580002_R08C01Naive200483200025_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 43).

 

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SubtitleTextAdding State 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 Set Attribute name: to shRNA treatmentSelect New Group to add an additional groupto Gender
  • Rename the three groups ControlshPOU5F1, and shNANOGgroups Male and Female
  • Drag and drop the samples from the Unassigned list to their groups as listed in the table below
Group NameGSM2515899200526580002_ControlGSM2515900200526580002ControlGSM2515901200526580002R03C01Control
Sample IDGender
GSM2452106_200483200025_R04C01Female
GSM2452107_200483200021_R01C01Female
GSM2452108_200483200021_R02C01Male
GSM2452109_200483200025_R06C01Female
GSM2515902GSM2452110_200526580002_R04C01Control

GSM2515903_200526580002_R05C01

shPOU5F1

GSM2515904_200526580002_R06C01

shPOU5F1

GSM2515905_200526580002_R07C01

shNANOG

GSM2515906_200526580002_R08C01

shNANOG200483200025_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 three two groups with four samples in each group Male and twelve samples in Female (Figure 54).

 

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SubtitleTextAdding shRNA treatment Gender attribute as a categorical attribute group
AnchorNameAdding shRNA treatment
State attribute

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

Two new column as columns have been added to spreadsheet 1 (Differential Methylation Analysiswith the state cell type and shRNA treatment gender of each sample (Figure 65). 

 

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Annotated beta values spreadsheet
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Annotated spreadsheet

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