Microarray analysis of gene expression in male germ cell tumors
The following tutorial walks through the identification of biological themes in a comparison of gene expression in normal testis to that in seminoma.

Before you begin, we recommend that you view the webinar. The top button on the right will take you to it if you haven't already.

When you are ready to begin this hands-on tutorial, click the third button down on the right, labeled "Log in to the dataset with tutorial."

1. Select Pairwise in the menu bar on the left side of the screen labeled Control Panel. You will see “Pairwise” under the heading marked “Analysis.”

2. Select the magnifying glass icon next to the top “ U133A” in the list (not the one marked as RMA normalized). This will examine the Affymetrix data.

3. At the top of the page is a list of the different experiment groups contained in this analysis. We’ll be comparing expression between the normal and pure seminoma samples. Select the 5 “Normal Testis” arrays to place them in Group 1.

4. Select the 12 “Seminoma” samples for Group 2.

5. Pairwise analysis combines a fold-change cutoff with a quality filter and comparison statistics to generate a list of differentially expressed genes. Select the following settings:

Normalization: None
The data has been normalized by MAS5, so no further normalization is required.

Statistics: t-test
Performs a two-sample, unpaired t-test for each gene that passes the quality and fold-change cutoffs.

Quality: P
Filters out genes that received absent or marginal detection calls in both groups. Click "Exclude Controls" to remove Affymetrix controls from analysis.

Threshold: Lower = 1.5; Upper = None.
Filters out genes with less than a 1.5 fold change in expression.

Correction: Benjamini and Hochberg
Calculates a false discovery rate from the raw p-values using the method of Benjamini and Hochberg.

Data transformation: Log Transform Data
This setting log base2 transforms the signal values.

6. Select the Analyze button.

7. At the top is a summary of the analysis just performed. The gene list below shows the genes that passed all our analysis parameters. By default, the most differentially expressed genes are shown first.

8. To filter the list using the adjusted p-value (false discovery rate), select “adjusted p” from the pull-down menu and then click the Search button.

9.The list filtered on the adjusted p-value contains genes with a false discovery rate less than 5% (all of the genes pass the more stringent cutoff of a false discovery rate less than 0.05).

10. To view data and a gene summary for any gene in the list, click the Gene Name. This will bring up a data summary and a One-Click Gene Summary™ (OCGS) for the gene. The One-Click Gene Summary provides a synopsis of current UniGene and Entrez Gene (formerly known as LocusLink) information for the gene.

11. Go back to the gene list by clicking the “Back” button in your browser.

12. Select the Ontology link at the top of the screen to view a summary of the Gene Ontology terms associated with the genes in the list. See the online help system for information about the other reports.

Note: To the view page-specific online help documents for any page, select the question mark icon located in the upper right corner of each page.

13. The Ontology Report lists the Gene Ontology terms associated with the genes in the pairwise results gene list. See the help documents for this page for more information about the Ontology Report.

14. Click on Z-score report in the upper right corner of the Ontology Report window.

15. The z-score report lists the biological process ontologies that are significantly over or under-represented in the gene list (z-score greater than 2 or less than -2, respectively). Select the red arrow in the z-score column (on the right of the screen) to sort the list by z-score for the up-regulated genes.

16. The z-score report shows that there is a significant enrichment of genes involved several biological processes, including protein biosynthesis, antigen processing and antigen processing. Near the middle of the list is the “immune response” ontology which is significantly over-represented in the upregulated gene list. Select the icon in the Genes column to view a list of the genes with this ontology.

Z-score reports can be generated for the Molecular Function and Cellular Component ontologies as well.

17. Return to the main GeneSifter window and click the KEGG link in the upper right corner. This will bring up a list of KEGG pathways that are significantly over or under-represented in the gene list. Select the red arrow in the z-score column (on the right of the screen) to sort the list by z-score for the up-regulated genes.

18. Click the icon in the KEGG column for the Ribosome pathway at the top of the list. This will bring up the KEGG diagram with differentially expressed genes highlighted in red.

Only a few specific aspects of the data set have been explored here. Feel free to examine the data further on your own.