T-test

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Overview

Note - as of 5/22/2006 this page is being redrafted, should be complete on 5/23/2006.

In this tutorial, you will:

  • Discuss the background for using Student's t-Test to evaluate data.
  • Load a predefined matrix format microarray dataset.
  • Select classes of arrays to compare.
  • Apply a t-Test.
  • Apply a multi t-test.
  • Visualize the results in the Color Mosaic and Volcano Plot components.
  • Note the creation of resulting marker lists and visualization objects.

Preparation

The file "webmatrix_quantile_log2_dev1_mv0.exp" is contained in the downloadable zip archive tutorial_data.zip. See the Download area.

For tips on loading data files, see the section Tutorial - Projects and Data Files.


t-Test

A t-Test analysis can be used to identify markers with statistically significant differential expression between two 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 sets, set the analysis parameters and view the results in the visualization components. The t-test implementation in geWorkbench offers several options for multiple testing correction and evaluation of the test statistic. A detailed description of the t-Test parameters is also available in online help.

P-value parameters:

The p-value can be estimated from 1. the t-statistic (the default) or 2. by permutation.

Alpha corrections

For multiple testing (alpha) correction, the following options are offered: 1. no correction 2. Standard Bonferonni Correction 3. Adjusted (step down) Bonferonni Correction. 4. Additional methods are available if the p-value is being estimated by permuation.

Degrees of Freedom

Group variances can be declared as: 1. unequal (Welch approximation) (the default) 2. Equal.


Classification

The desired sets of arrays should be activated in the Arrays/Phenotypes component. This is done by checking the boxes by the desired Sets.

T t-test Set selection BCELL.png


The t-test requires two groups of microarrays to compare. geWorkbench distinguishes the two groups by one being labeled as "Case". By default, all others are considered as control. Note that in the Arrays/Phenotypes component, more than one set of arrays can be marked "Case". All remaining (activated) arrays will then be in the "Control" group.


T t-test set Case BCELL webm qldm.png


The thumbtack image next to activated Array Sets is colored red.


T t-test Arrays case-set BCELL webm qldm.png


Set Analysis Parameters

  1. From the Analysis Panel, select T-Test Analysis.
  2. Various parameters can be adjusted as desired. Here we will use the default parameters:
  • Alpha-corrections tab: Just Alpha.

T t-test bonferroni BCELL webm qldm.png


  • P-Value Parameters tab: p-values based on t-distribution. Note that the default alpha (critical p-value) is set to 0.01.

T t-test p-values.png


  • Degree of Freedom tab: Welch approximation - unequal group variances.

T t-test dof.png


After all the parameters have been set, just click Analyze. The results will be returned in three locations: The Project Folder, the Markers component, and the Visualization area.

t-Test Results

The result is placed into the Projects Folder as a child of the microarray dataset that was used.

T t-test ProjectFolders result.png


The results are displayed by default using the Volcano Plot visualizer.

T t-test volcano BCELL webm qldm.png


The adjancent tab provides a Color Mosaic showing all of the arrays and the p-value calculated for each marker. It also can display annotation for each marker.

T t-test colormosaic BCELL webm qldm.png


The set of markers which met the minimum signifcance paramter are placed into a new Marker Set labeled "Significant Genes" in the Markers component.

T t-test Markers BCELL result.png


Multi t-test

  • The Multi t-test component allows more than two groups to be compared simultaneously. Its shows each Array Set that has been defined. It will compare in pairwise fashion all selected sets.
  • A step-down Bonferonni type correction is used to account for multiple testing of markers.
  • As currently implemented, it does not correct for multiple pairwise t-tests being run on the various array sets.
  • Results can be viewed in the Volcano Plot and in the Color Mosaic components.

Set selection for the multi-t-test is handled independently of the Arrays/Phenotypes component. (This feature may be phased out, as it is not standard).

T multi-t-test Set selection BCELL.png


The results of each pairwise t-test are placed into the Project Folder. Each can be viewed by selecting it there. T multi-t-test ProjectFolders result.png


References

t-test [1]

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