T-test - Revision history

Smith: /* Fold Change */

2015-03-13T20:02:51Z

‎Fold Change

2015-03-12T22:36:15Z

‎Overview

2015-03-12T19:59:42Z

2015-03-11T21:00:48Z

‎Preparation

2015-03-11T20:37:16Z

‎Preparation

2015-03-11T20:22:48Z

‎Overview

2015-03-11T20:22:17Z

2015-03-11T19:20:08Z

2014-01-22T20:24:01Z

‎Setting the Analysis Parameters

2014-01-22T20:21:00Z

‎Setting the Analysis Parameters

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 The method used to calculate fold change depends on whether the data was marked as log2 transformed or not during the t-test using the "Data is log2 transformed" box.
-* '''Linear data''' ("Data is log2 transformed" box '''was not''' checked): the fold change is calculated, for each marker, as the Log2 transform of the average expression in the Case set divided by the average expression in the control set, that is, Log2(Avg(cases)/Avg(controls)) or Log2(Avg(cases)) - Log2(Avg(controls)).  (Difference of logs of averaged values).
+* '''Linear data''' ("Data is log2 transformed" box '''was not''' checked): the fold change is calculated, for each marker, as the Log2 transform of the average expression in the Case set divided by the average expression in the control set, that is,
-* '''Log2 transformed data''' ("Data is log2 transformed" box '''was''' checked): In this case, for each marker, the average of the (log) case values minus the average of the (log) control values is calculated, that is, Avg(cases) - Avg(controls).  (Difference of averaged log values).
+* '''Log2 transformed data''' ("Data is log2 transformed" box '''was''' checked): In this case, for each marker, the average of the (log) case values minus the average of the (log) control values is calculated, that is,
 The fold change is not calculated if, for the linear case, the average case or control value is negative.

← Older revision		Revision as of 22:36, 12 March 2015
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	=Overview=		=Overview=
		+
		+	For the '''geWorkbench web''' version of T-Test please see [[T-Test_web]].

	A t-Test analysis can be used to identify markers with statistically significant differential expression between two sets of microarrays. In geWorkbench, these groups are specified as the "Case" and "Control" sets.		A t-Test analysis can be used to identify markers with statistically significant differential expression between two sets of microarrays. In geWorkbench, these groups are specified as the "Case" and "Control" sets.

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 ==Alpha corrections==
-For multiple testing (alpha) correction, the following options are offered:
+Several  methods for correcting for the effects of performing multiple tests are offered, including Bonferroni and False Discovery Rate control.  They differ in how they compare the calculated p-value to the cutoff value of alpha - the critical p-value for determining the significance of an observed difference.
-* '''no correction'''
-* '''Standard Bonferonni Correction''' - the value of alpha is divided by the number of markers included in the analysis.
+* '''Just Alpha''' - No correction is performed.
-* '''Adjusted (step down) Bonferonni Correction'''
+* '''Standard Bonferroni''' - The cutoff value (alpha) is divided by the number of tests (genes) before being compared with the calculated p-values.
 * Two variants of the Westfall and Young method are available if the p-value is estimated by permuation:
 ** '''minP'''

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 ==Preparation==
-Obtain the file "BCell-100.exp", which is contained in the data/public_data directory of the geWorkbench distribution, or can be directly downloaded from the tutorial data [[Download_and_Installation#Tutorial_data | download]] area.  A version with the threshold normalizer and log2 transformations described below  is also available there.
+This example uses the file [[Media:Bcell-100.zip|Bcell-100.zip]], which also is contained in the data/public_data directory of the geWorkbench distribution, and is further described in the tutorial data [[Tutorial_Data | Tutorial_Data]] area.  [[Media:Bcell-100_log2.zip|A version]] with the threshold normalizer and log2 transformations described below already applied is also available there.
 You may also wish to load the Affymetrix HG-U95Av2 annotation file, although it is not required for this example.  See the [[FAQ | FAQ]] section for information on downloading this file from Affymetrix.

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 ==Preparation==
-Obtain the file "BCell-100.exp", which is contained in the data/public_data directory of the geWorkbench distribution, or can be directly downloaded from the tutorial data [[Download_and_Installation#Tutorial_data | download]] area.
+Obtain the file "BCell-100.exp", which is contained in the data/public_data directory of the geWorkbench distribution, or can be directly downloaded from the tutorial data [[Download_and_Installation#Tutorial_data | download]] area.  A version with the threshold normalizer and log2 transformations described below  is also available there.
 You may also wish to load the Affymetrix HG-U95Av2 annotation file, although it is not required for this example.  See the [[FAQ | FAQ]] section for information on downloading this file from Affymetrix.

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 There are several steps to setting up a t-test analysis in geWorkbench.
-# At least two sets of arrays must be available in the [[Data_Subsets_-_Arrays | Arrays]] component.
+# At least two sets of arrays must be available in the [[Array_Sets | Arrays]] component.
 # The array sets to be used in the analysis must be "activated" by checking the box adjacent to their names in the [[Array_Sets | Arrays]] component.
 # One or more activated array sets must be designated "Case", and the others "Control" (which is the default classification).

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 There are several steps to setting up a t-test analysis in geWorkbench.
 # At least two sets of arrays must be available in the [[Data_Subsets_-_Arrays | Arrays]] component.
-# The array sets to be used in the analysis must be "activated" by checking the box adjacent to their names in the [[Data_Subsets_-_Arrays | Arrays]] component.
+# The array sets to be used in the analysis must be "activated" by checking the box adjacent to their names in the [[Array_Sets | Arrays]] component.
 # One or more activated array sets must be designated "Case", and the others "Control" (which is the default classification).
 # The t-test parameters must be set.
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 After the t-test is run, the results will be displayed graphically, and all markers meeting the significance threshold are placed into a new Marker Set called "Significant Genes".
-Please see the [[Differential_Expression#Example | Example]] section below for instructions on preparing array sets for the t-test analysis.
+Please see the [[T-test#Example | Example]] section below for instructions on preparing array sets for the t-test analysis.
 As of geWorkbench 2.4.0, the t-test result is calculated using the Apache Commons Math Library.

← Older revision	Revision as of 19:20, 11 March 2015
(No difference)

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 ==Setting the Analysis Parameters==
 # The t-test component should be loaded by default in the [[Component_Configuration_Manager|Component Configuration Manager]].
 # From the Analysis Panel, select '''T-Test Analysis'''.

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 ## P-values based on t-distribution.
 ## Note that here the default alpha (critical p-value) is set to 0.01.
-## Check-mark the box "Data is log2 Transformed".
+## If the data has been log2 transformed, check-mark the box "Data is log2 Transformed".
 # Alpha-corrections tab
 ##  Standard Bonferonni