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• Select the Add File(s) > button to move all the .CEL files to the right panel. Twenty-five CEL files will be processed
• Select the Next > button to open the Import Affymetrix CEL Files dialog (Figure 3)

Figure 3: Import Affymetrix CEL Files dialog

• Select Customize… to open the Advanced Import Options dialog (Figure 4) 

Figure 4: Advanced Import Options

• Select Library Files… to open the Specify File Locations dialog (Figure 5). This dialog is used to specify the location of the library folder and the annotation files 

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• The default library location can be modified at by selecting Change... in the Default Library File Folder panel. By default, the library directory is at C:\Microarray Libraries. This directory is used to store all the external libraries and annotation files needed for analysis and visualization. The library directory can also be modified from Tools > File Manager in the main PGS menu
• Select OK (Figure 5) to close the Specify File Locations dialog

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• Select the Outputs tab from the Advanced Import Options dialog (Figure 6)

Figure 6: 

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 After importing the .CEL files has finished, the result file will open in PGS as a spreadsheet named 1 (Down_Syndrome-GE). The spreadsheet should contain 25 rows representing the micoarray chips (samples) and over 22,000 columns representing the probe sets (genes) (Figure 7). 

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In this tutorial, the file name (e.g., Down Syndrome-Astrocyte-748-Male-1-U133A.CEL) contains the information about a sample and is separated by hyphens (-). Choosing to split the file name by delimiters will separate the categories into different columns as shown in (Figure 118).

• In the Sample Information panel, specify the column labels (Labels 1-4) as Type, Tissue, Subject, and Gender, set each as categorical, and set the other columns as skip (see Figure 118). Select OK

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• A dialog window asking if you would like to save the spreadsheet with the new sample attribute will appear. Select Yes
• Make column 5. (Subject) random by right-clicking on the column header and selecting Properties from the pop-up menu (Figure 9). Select the Random Effect check box from the Properties dialog then select OK. The column 5. (Subject) will now be colored red, indicating that it is a random effect. 

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• Select Plot PCA Scatter Plot from the QA/AC section of the Gene Expression workflow. A Scatter Plot tab containing your PCA plot will open (Figure 10)

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• In the Scatter Plot tab, select the Rendering Properties icon () and configure the plot as shown (Figure 11)
• Color the points by column 4. Tissue and Size the points by column 3. Type
• Select OK
  

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Notice now that the data are clustered by different tissues (Figure 12). 

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By rotating this PCA plot, you can see that the data is separated by tissues, and within some of the tissues, the Down syndrome samples and normal samples are separated. For example, in the Astrocyte and Heart tissues, the Down syndrome samples (small dots) are on the left, and the normal samples (large dots) are on the right (Figure 13).

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The next step is to draw a histogram to examine the samples. Select Plot Sample Histogram in the QA/QC section of the Gene Expression workflowto generate the Histogram tab as shown in Figure 18(Figure 14).

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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 15). Do NOT select OK or Apply. We will be adding contrasts to this ANOVA in an upcoming section of the tutorial. 

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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 16)
• 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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• 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 in (Figure 19 15) 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 the child result spreadsheet, each row represents a gene, and the columns represent the computation results for that gene (Figure 17). 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.

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

Viewing the Sources of Variation

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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 18) 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

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• Under the Visualization section in the Gene Expression workflow, select Cluster Based on Significant Genes
• The Cluster Significant Genes dialog asks you to specify the type of clustering you want to perform. Select Hierarchical Clustering and select OK
• Choose the Down_Syndrome_vs_Normal (A)  spreadsheet under the Spreadsheet with differentially expressed genes 
• Choose the Standardize – shift genes to mean of zero and scale to standard deviation of one under the Expression normalization panel. This option will adjust all the gene intensities such that the mean is zero and the standard deviation is 1
• Select OK to generate a Hierarchical Clustering tab (Figure 20)

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Figure 20: Hierarchical Clustering results

The graph (Figure 20) illustrates the standardized gene expression level of each gene in each sample. Each gene is represented in one column, and each sample is represented in one row. Genes which are unchanged are have a value of zero and are colored black. Genes with increased expression have positive values and are colored red. Genes with reduced expression have negative values and are colored green. Down syndrome samples are colored red and normal samples are colored orange. On the left-hand side of the graph, we can see that the Down syndrome samples cluster together.

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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 21)
• Select Chromosomal Locationunder the Column Configuration panel. Leave everything else as default and select OK 

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• To create a dot plot showing expression levels of a specific gene for each sample, right click on the row header and select Dot Plot (Orig. Data) from the pop-up menu. This generates a Dot Plot tab for the selected gene (Figure 22)

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• Ensure that the 1/ANOVA-3way (ANOVAResults) spreadsheet in the Analysis tab is selected as 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 23)