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• Select Contrasts… to invoke the Configure dialog
• Choose 6.Type from the Select Factor/Interaction drop-down list. The levels in this factor are listed on the Candidate Level(s) panel on the left side of the dialog (Figure 1617)
• Left click to select Down Syndrome from the Candidate Level(s) panel and move it to the Group 1 panel (renamed Down Syndrome) by selecting Add Contrast Level> in the top half of the dialog. Label 1 will be changed to the subgroup name automatically, but you can also manually specify the label name
• Select Normal from the Candidate Level(s) panel and move it to the Group 2panel (renamed Normal)

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Figure 17: Configuring contrasts for ANOVA

Because the data is log₂ transformed, PGS will automatically detect this and will automatically select Yes in the Data is already log transformed? at the top right-hand corner. PGS will use the geometric mean of the samples in each group to calculate the fold change and mean ratio for the contrast between the Down syndrome and Normal samples.

• Select Add Contrast to add the Down Syndrome vs. Normal contrast 
• Select OK to apply the configuration
• If successfully added, the Contrasts… button will now read Contrasts Included
• By default, the Specify Output File is checked (Figure 17) and gives a name to the output file. If you are trying to determine which factors should be included in the model and you do not wish to save the output file, simply uncheck this box
• Select OK in the ANOVA dialog to compute the 3-way mixed-model ANOVA
• Several progress messages will display in the lower left-hand side of the ANOVA dialog while the results are being calculated.

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Figure 17: Configuring contrasts for ANOVA

The result will be displayed in a child spreadsheet, ANOVA-3way (ANOVAResults). In the child result spreadsheet, each row represents a gene, and the columns represent the computation results for that gene (Figure 18). By default, the genes are sorted in ascending order by the p-value of the first categorical factor. In this tutorial,Type is the first categorical factor, which means the most highly significant differently expressed gene between Down syndrome and normal samples is at the top of the spreadsheet in row 1.

Figure 18: ANOVA spreadsheet

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• View the sources of variation for each of the factors across the whole genome by clicking Plot Sources of Variation from the Analysis section of the Gene Expression workflow with the ANOVA result spreadsheet active
• A Sources of Variation tab will appear (Figure 1819) with a bar chart showing the signal to noise ratio for each factor. Sources of variation can also be viewed as a pie chart showing sum or squares by selecting the Pie Chart (Sum of Squares) tab in the upper left-hand side of the Sources of Variation tab

Figure 19: Sources of Variation tab showing a bar chart

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• In the Down_Syndrome_vs_Normal (A) spreadsheet, right click on the second column header 2. ProbesetID and select Insert Annotation from the pop-up menu (Figure 23)
• Select Chromosomal Locationunder the Column Configuration panel. Leave everything else as default and select OK

Figure 23: Adding a gene annotation 

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• Select the 1/ANOVA-3way (ANOVAResults) spreadsheet in the Analysis tab. This is the spreadsheet we will be using to create the gene list
• Select View > Volcano Plot from the PGS main menu
• Set X Axis (Fold-Change) to 12. Fold-Change(Down Syndrome vs. Normal), and the Y axis (p-value) to be 110. p-value(Down Syndrome vs. Normal)
• Select OK to generate a Volcano Plot tab and for genes in the spreadsheet (Figure 2325)

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• Select Rendering Properties ()
• Choose the Axes tab
• Select the Set Cutoff Lines button and configure the Set Cutoff Lines dialog as shown (Figure 2426)
• Check Select all points in a section to allow PGS to automatically select all the points in any given section
• Select OK to draw the cutoff lines
• Select OK in the Plot Rendering Properties dialog to close the dialog and view the plot

Figure 24: Setting cutoff lines for -1.3 to 1.3 fold changes and p value of 0.05

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