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• To invoke the ANOVA dialog, select Detect Differentially Expressed Genes in the Analysis section of the Gene Expression workflow
• In the Experimental Factor(s) panel, select Type, Tissue and Subject by pressing <Ctrl> and left clicking each factor
• Use the Add Factor > button to move the selections to the ANOVA Factor(s) panel
• To specify the interaction, select Type and Tissue by pressing <Ctrl> and left clicking each factor. Select the Add Interaction > button to add the Type * Tissue interaction to the ANOVA Factor(s) panel (Figure 1). Do NOT select OK or Apply. We will be adding contrasts to this ANOVA model in an upcoming section of the tutorial. 
  
  

Random vs. Fixed Effects – Mixed Model ANOVA

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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
• 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 2 panel (renamed Normal) 
• The Add Contrast button can now be selected (Figure 2)
  
  

Because the data is log₂ transformed, Partek Genomics Suite will automatically detect this and will automatically select Yes for Data is already log transformed? in the top right-hand corner of the dialog. Partek Genomics Suite 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 (Figure 3)
  
  

• By default, Specify Output File is checked 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.

The result will be displayed in a child spreadsheet, ANOVA-3way (ANOVAResults). In this spreadsheet, each row represents a gene and the columns represent the computation results for that gene (Figure 4). 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.

For additional information about ANOVA in Partek Genomics Suite, see Chapter 11 Inferential Statistics in the User’s Manual (Help > User’s Manual).

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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 5) 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
  
  

This plot presents the mean signal-to-noise ratio of all the genes on the microarray. All the non-random factors in the ANOVA model are listed on the X-axis (including error). The Y-axis represents the mean of the ratios of mean square of all the genes to the mean square error of all the genes. Mean square is ANOVA’s measure of variance. Compare the bar for each signal to the bar for error; if a factor's bar is higher than error's bar, that factor contributed significant variation to the data across all the variables. Notice that this plot is very consistent with the results in the PCA scatter plot. In this data, on average, Tissue is the largest source of variation.

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• Right-click on a row header in the ANOVA spreadsheet (Figure 6)
• Select ANOVA Interaction Plot to generate an Interaction Plot tab for that individual gene
  
  

Generate these plots for rows 3 (DSCR3) and 8 (CSTB). If the lines in the interaction plot are not parallel, then there is a chance that there is an interaction between Tissue and Type. DSCR3 is a good example of this (Figure 7). We can look at the p-values in column 9, p-value(Type * Tissue) to check if this apparent interaction is statistically significant. 

We can view the expression levels of a gene for each sample using a dot plot.

• Right click on the gene row header and select Dot Plot (Orig. Data) from the pop-up menu. This generates a Dot Plot tab for the selected gene (Figure 8)
  
  

In the plot, each dot is a sample of the original data. The Y-axis represents the log₂ normalized intensity of the gene and the X-axis represents the different types of samples. The median expression of each group is different from each other in this example. The median of the Down syndrome samples is ~6.3, but the median of the normal samples is ~6.0. The line inside the Box & Whiskers represents the median of the samples in a group. Placing the mouse cursor over a Box & Whiskers plot will show its median and range. 

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• Invoke the List Manager dialog by selecting Create Gene List in the Analysis section of the Gene Expression workflow
• Ensure that the 1/ANOVA-3way (ANOVAResults) spreadsheet is selected as this is the spreadsheet we will be using to create our new gene list as shown (Figure 9)
• Select the ANOVA Streamlined tab. In the Contrast: find genes that change between two categories panel, choose Down Syndrome vs. Normal and select Have Any Change from the Setting dropdown menu list. This will find genes with different expression levels in the different types of samples
• In the Configuration for “Down Syndrome vs Normal” panel, check that Include size of the change is selected and enter 1.3 into Fold change >  and -1.3 in OR Fold change <
• Select Include significance of the change, choose unadjusted p-value from the dropdown menu, and < 0.0005 for the cutoff. The number of genes that pass your cutoff criteria will be shown next to the # Pass field. In this example, 23 genes pass the criteria. 
• Set Save the list as A, select Create, and then select Close to view the new gene list spreadsheet
  
  

The spreadsheet Down_Syndrome_vs_Normal (A) will be created as a child spreadsheet under the Down_Syndrome-GE spreadsheet.

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• Select the 1/ANOVA-3way (ANOVAResults) spreadsheet in the Analysis tab. This is the spreadsheet our gene list will be drawn from
• Select View > Volcano Plot from the Partek Genomics Suite main menu (Figure 10)
  
  

• 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 for genes in the ANOVA spreadsheet (Figure 11)
  
  

In the plot, each dot represents a gene. The X-axis represents the fold change of the contrast (Down syndrome vs. Normal), and the Y-axis represents the range of p-values. The genes with increased expression in Down syndrome samples are on the right side of the N/C (no change) line; genes with reduced expression in Down syndrome samples are on the left. The genes become more statistically significant with increasing Y-axis position. The genes that have larger and more significant changes between the Down syndrome and normal groups are on the upper right and upper left corner. 

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• Select Rendering Properties ()
• Choose the Axes tab
• Check Select all points in a section to allow Partek Genomics Suite to automatically select all the points in any given section
• Select the Set Cutoff Lines button and configure the Set Cutoff Lines dialog as shown (Figure 12)
  
  

• Select OK to draw the cutoff lines
• Select OK in the Plot Rendering Properties dialog to close the dialog and view the plot

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• Right-click on the selected region in the plot and choose Create List to create a list including the genes from the section selected (Figure 13). Note that these p-values are uncorrected
  
  

Note: If no column is selected in the parent (ANOVA) spreadsheet, all of the columns will be included in the gene list; if some columns are selected, only the selected columns will be included in the list.

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 The list can be saved as a text file (File > Save As Text File) for use in reports or by downstream analysis software.

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