T-test
In this tutorial, you will:
- Get acquainted with the T Test and Multi T Test
- Apply a T Test and Multi T Test
Before you can continue, geworkbench should be running. Load the data as described in Tutorial - Projects and Data Files.
T Test
T Test analysis identifies markers with statistically significant differential expression between sets of microarrays. The t-test determines for each marker if there is a significant difference between the two groups (case and control). To perform this analysis, you must classify the panels, set the analysis parameters and view the results in the visualization components. A detailed description of the T Test parameters is described in online help.
Classify the Sets
This process has already been described in Tutorial - Data Subsets. Briefly,
1. Mark the Cardio phenotype a 'Case'. By default, panels are marked as control. Panels classified Case are shown with a red thumbtack icon.
- Right-click on Cardio phenotype.
- Select Classification>Case.
2. Activate the arrays Normal and CCMP by selecting the checkboxes next to the panel name.
Set Analysis Parameters
- From the Analysis Panel, select T-Test Analysis.
- Populate the below parameters values and click on Analyze.
- Alpha-corrections tab: Just Alpha.
- P-Value Parameters tab: p-values based on t-distribution. Note that the default alpha (critical p-value) is set to 0.01.
- Degree of Freedom tab: Welch approximation - unequal group variances.
T-Test Results
Markers which met the significance test are included in a new gene panel called “Significant Genes”. | ||
Ancillary dataset is created in the project window. |
The values of the T-Test can be seen in the Color Mosaic panel and the Volcano Plot.