Androgen regulation of gene expression in the mouse lacrimal gland
The following tutorial walks through the analysis results presented in:

Richards SM, Liu M, Jensen RV, Schirra F, Yamagami H, Lombardi MJ, Rowley P, Treister NS, Suzuki T, Sullivan BD, Sullivan DA. Androgen regulation of gene expression in the mouse lacrimal gland. . J Steroid Biochem Mol Biol.. 2005 Sep;96(5):401-13.

When you are ready to begin this hands-on tutorial, click the second 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 “Androgen studies” in the list. The data examined here was generated using the CodeLink Uniset I 10K Mouse array. There are approximately 10,000 transcripts represented on this array.

3. This example will compare gene expression in lacrimal glands from placebo treated orchiectomized male mice to that in glands from testosterone treated orchiectomized mice. Click the check boxes to select the three replicates for the three placebo treated gland samples (Male Placebo Lac) for Group 1 and the three testosterone treated gland samples (Male Test Lac) for Group 2.

4. 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
Data was already normalized using CodeLink software and then loaded into GeneSifter.

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

Quality: 0.75
Filters out low intensity spots.

Threshold*: Lower = None; Upper = None.

Correction: None

Data transformation: No transformation

*This tutorial walks through the analysis used to generate figure 1-4 (Richards et al., J Steroid Biochem Mol Biol. 2005 Sep;96(5):401-13). Change the threshold to limit gene list based on fold change.

5. Select the Analyze button.

6. After the analysis is performed a gene list will be returned. This list contains the genes that passed all the analysis parameters. The genes are sorted by fold-change with the most changed genes shown first.

7. To view data and a gene summary for any gene in the list, click the Gene Name.

8. 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.

9. The Ontology Report lists the Gene Ontology terms associated with the 2109 genes in the pairwise results gene list. See the help documents for this page for more information about the Ontology Report.

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

11. 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). 13 (for the example shown, the ontology “macromolecule biosynthesis” ” is significantly over-represented for the genes with lower expression in testosterone treated mice). 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.

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

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