Difference between revisions of "T-test"
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__TOC__
 
__TOC__
  
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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:  
 
In this tutorial, you will:  
  
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* Get acquainted with the t-Test and Multi t-Test
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* Discuss the background for using Student's t-Test to evaluate data.
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* Apply a t-Test and Multi t-Test
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* Load a predefined matrix format microarray dataset.
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* Select classes of arrays to compare.
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* Apply a t-Test.
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* Apply a multi t-test.
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* Visualize the results in the Color Mosaic and Volcano Plot components.
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* Note the creation of resulting marker lists and visualization objects.
  
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Before you can continue, geworkbench should be running.  Load the data as described in [[Tutorial - Projects and Data Files]].
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==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]].
  
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==t-Test==
 
  
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A t-Test analysis can be used to identify 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 sets, 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.
 
  
 +
==t-Test==
  
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===Classify the Sets===
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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. A detailed description of the t-Test parameters is also available in online help. The implementation in geWorkbench offers several options for multiple testing correction and evaluation of the test statistic.
  
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This process has already been described in [[Tutorial - Data Subsets]].  Briefly,
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===Classification===
  
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1. Mark the '''Cardio''' phenotype a 'Case'. By default, sets are marked as control.  Sets classified Case are shown with a red thumbtack icon.
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The desired sets arrays should be activated in the Arrays/Phenotypes component.
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* Right-click on '''Cardio''' phenotype.
 
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* Select '''Classification'''>'''Case'''. 
 
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2. Activate the arrays '''Normal''' and '''CCMP''' by selecting the checkboxes next to the set name.
 
  
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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 labeled 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.
  
  
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[[Image:T_Arrays_SetCase.png]]
 
  
 
===Set Analysis Parameters===
 
===Set Analysis Parameters===
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* P-Value Parameters tab: p-values based on t-distribution. Note that the default alpha (critical p-value) is set to 0.01.
 
* 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.  
 
* Degree of Freedom tab: Welch approximation - unequal group variances.  
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[[Image:Ttest.gif]]<br>
 
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===t-Test Results===
 
===t-Test Results===
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T_multi-t-test_ProjectFolders_result.png
  
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T_multi-t-test_Set_selection_BCELL.png
  
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T_t-test_Arrays_case-set_BCELL_webm_qldm.png
  
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{|style="border: 1px solid lightGray"
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T_t-test_bonferroni_BCELL_webm_qldm.png
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!|| ||
 
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|-
 
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|-
 
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|  Markers which met the significance test are included in a new Marker Set called “Significant Genes”.  || [[Image:E_ttestgpanel.png]]
 
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|-
 
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|-
 
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| Ancillary dataset is created in the project window. ||  [[Image:Ed_ttestproj.png]]
 
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|-
 
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|}
 
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The values of the t-Test can be seen in the Color Mosaic panel and the Volcano Plot.
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T_t-test_colormosaic_BCELL_webm_qldm.png
  
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T_t-test_dof.png
  
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{|style="border: 1px solid lightGray"
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T_t-test_Markers_BCELL_result.png
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!VOLCANO PLOT||COLOR MOSAIC||
 
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|-
 
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|-|-
 
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|-
 
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|-
 
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| [[Image:Vplot.png]]  ||  [[Image:Ed_cm.png]]
 
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|-
 
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|-
 
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|-
 
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| Clicking on any of the spots highlights the marker selected in the Marker component.  * Insert another description || 
 
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* The label to the right displays the Significance value ( lower the value, most likely different) and gene name for the displayed genes. The genes are displayed in ascending order by Significance Value.
 
  
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* Gene height and width values can be altered to modify the display.
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T_t-test_ProjectFolders_result.png
  
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* The intensity slider is used to modify the intensity of the color codings.  
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T_t-test_p-values.png
  
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* Accession: Includes the accesion number in the label.
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T_t-test_set_Case_BCELL_webm_qldm.png
  
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* Printer Icon: Prints the displayed image.  
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T_t-test_Set_selection_BCELL.png
  
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* Display: Must be toggled on to display data.
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T_t-test_volcano_BCELL_webm_qldm.png
  
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*  ''Pat, Abs, Ratio and Overlapping Pages Icons: These are not relevant to the t-Test display.''
 
  
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|-
 
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|-
 
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|}
 
  
 
==Multi t-test==
 
==Multi t-test==
  
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*The Multi t-test component allows more than two groups to be compared simultaneously.  Its shows each Marker Set that has been defined.  It will compare all selected sets against all other selected sets.
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*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.  
  
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*A step-down Bonferonni type correction is used to account for multiple testing.
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* A step-down Bonferonni type correction is used to account for multiple testing of markers.
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*Results can be viewed in the Volcano Plot and in the Color Mosaic components.
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* 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.
  
  

Revision as of 18:00, 22 May 2006

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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. A detailed description of the t-Test parameters is also available in online help. The implementation in geWorkbench offers several options for multiple testing correction and evaluation of the test statistic.

Classification

The desired sets arrays should be activated in the Arrays/Phenotypes component.

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 labeled 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.


Set Analysis Parameters

  1. From the Analysis Panel, select T-Test Analysis.
  2. 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

T_multi-t-test_ProjectFolders_result.png

T_multi-t-test_Set_selection_BCELL.png

T_t-test_Arrays_case-set_BCELL_webm_qldm.png

T_t-test_bonferroni_BCELL_webm_qldm.png

T_t-test_colormosaic_BCELL_webm_qldm.png

T_t-test_dof.png

T_t-test_Markers_BCELL_result.png

T_t-test_ProjectFolders_result.png

T_t-test_p-values.png

T_t-test_set_Case_BCELL_webm_qldm.png

T_t-test_Set_selection_BCELL.png

T_t-test_volcano_BCELL_webm_qldm.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.


References

t-test [1]

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