Tutorial - Reverse Engineering - Revision history

Smith at 23:08, 21 November 2006

2006-11-21T23:08:25Z

Smith at 23:02, 21 November 2006

2006-11-21T23:02:31Z

Smith at 23:01, 21 November 2006

2006-11-21T23:01:08Z

Smith at 17:35, 21 August 2006

2006-08-21T17:35:29Z

Smith: /* Limitations */

2006-08-21T17:32:05Z

‎Limitations

Smith: /* Prerequisites */

2006-08-17T18:45:44Z

‎Prerequisites

Smith: /* Limitations */

2006-08-17T18:44:59Z

‎Limitations

Smith: /* Outline */

2006-08-17T18:44:41Z

‎Outline

Smith at 18:44, 17 August 2006

2006-08-17T18:44:08Z

Smith: /* Limitations */

2006-08-14T22:52:06Z

‎Limitations

@@ Line 95: / Line 95: @@
 We can return to the main Reverse Engineering component by clicking on the original dataset in the Project Folders component.  If we select the first (highest MI score) marker on the list, the graph shown below is drawn in the '''Motif Location Histogram''' display. This shows a plot of the expression values on each array for the selected hub marker vs any other marker selected in the list.
-#. The Motif Location Histogram plot is only drawn if you select "All Arrays" or activate a set of arrays in the Arrays/Phenotypes component.  If you select "All Arrays", then all the points will be one color.  If you activate individual sets of arrays, each set will be drawn in a different color.
+#. The Motif Location Histogram plot is only drawn if you select "All Arrays" or activate a set of arrays in the Arrays/Phenotypes component.  If you select "All Arrays", then all the points will be one color:
-[[Image:T_ReverseEngineering_MotifHistogram.png]]
+[[Image:Motif_Location_Histogram_Sets.png]]
 ==Options==
 '''Pearson''' - Uses a Pearson correlation function to calculate the interaction scores.

← Older revision		Revision as of 23:02, 21 November 2006
Line 95:		Line 95:
	We can return to the main Reverse Engineering component by clicking on the original dataset in the Project Folders component. If we select the first (highest MI score) marker on the list, the graph shown below is drawn in the '''Motif Location Histogram''' display. This shows a plot of the expression values on each array for the selected hub marker vs any other marker selected in the list.		We can return to the main Reverse Engineering component by clicking on the original dataset in the Project Folders component. If we select the first (highest MI score) marker on the list, the graph shown below is drawn in the '''Motif Location Histogram''' display. This shows a plot of the expression values on each array for the selected hub marker vs any other marker selected in the list.

−	#. The Motif Location Histogram plot is only drawn if you select "All Arrays" or activate a set of arrays in the Arrays/Phenotypes component.	+	#. The Motif Location Histogram plot is only drawn if you select "All Arrays" or activate a set of arrays in the Arrays/Phenotypes component. If you select "All Arrays", then all the points will be one color. If you activate individual sets of arrays, each set will be drawn in a different color.

← Older revision		Revision as of 23:01, 21 November 2006
Line 95:		Line 95:
	We can return to the main Reverse Engineering component by clicking on the original dataset in the Project Folders component. If we select the first (highest MI score) marker on the list, the graph shown below is drawn in the '''Motif Location Histogram''' display. This shows a plot of the expression values on each array for the selected hub marker vs any other marker selected in the list.		We can return to the main Reverse Engineering component by clicking on the original dataset in the Project Folders component. If we select the first (highest MI score) marker on the list, the graph shown below is drawn in the '''Motif Location Histogram''' display. This shows a plot of the expression values on each array for the selected hub marker vs any other marker selected in the list.

−	#. ~~Currently,~~ you ~~must~~ select "All Arrays" ~~to draw this plot~~.	+	#. The Motif Location Histogram plot is only drawn if you select "All Arrays" or activate a set of arrays in the Arrays/Phenotypes component.

← Older revision		Revision as of 17:35, 21 August 2006
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	{{TutorialsTopNav}}		{{TutorialsTopNav}}
		+
		+	==Note==
		+
		+	Please see also the beta-release in geWorkbench of the [[Tutorial_-_ARACNE \| ARACNE]] algorithm for network reverse engineering. This is a more complete implementation than is present in the Reverse Engineering component.

	==Outline==		==Outline==

@@ Line 18: / Line 18: @@
 ==Limitations==
 # The "Motif Location Histogram" only displays if the "All Arrays" checkbox is checked.  This is being fixed.
 # The Score vs. Probability graph does not work.  This is being fixed.
 # There is some overlap in functionality between Profiler tab and the Conditional Analysis tab.  Both calculate aspects of conditional interactions.
 # There is not yet a tutorial for the Conditional Analysis tab.
 # The images shown below were calculated using a slightly larger dataset than is currently being distributed with the tutorials.  '''The images will be updated soon.'''

@@ Line 26: / Line 26: @@
 ==Prerequisites==
-A dataset  containing multiple arrays (the more the better) should be loaded into geWorkbench.  If data is loaded from separate files, it should be merged into a single microarray datset, either at the time of or after being read in.  See the section  [[Tutorial - Projects and Data Files | Projects and Data Files]].  In this tutorial we will load a dataset also used in other tutorial sections, which has been normalized, and filtered to reduce the number of genes.  This file, "webmatrix_quantile_log2_dev1.2_mv0.exp" is available in the tutorial data section.  Its derivation is described in Tutorial_-_Data  [[Tutorial_-_Data | Data]]
+A dataset  containing multiple arrays (the more the better) should be loaded into geWorkbench.  If data is loaded from separate files, it should be merged into a single microarray datset, either at the time of or after being read in.  See the section  [[Tutorial - Projects and Data Files | Projects and Data Files]].  In this tutorial we will load a dataset also used in other tutorial sections, which has been normalized, and filtered to reduce the number of genes.  This file, "webmatrix_quantile_log2_dev1.2_mv0.exp" is available in the tutorial data section.  Its derivation is described in [[Tutorial_-_Data | Data]]
 ==Example - Profiler==

@@ Line 3: / Line 3: @@
 ==Outline==
 In this tutorial, we will cover:
-#. Background to gene network reverse engineering
+# Background to gene network reverse engineering
-#. Calculate the mutual information between a hub gene and the others in a dataset
+# Calculate the mutual information between a hub gene and the others in a dataset
-#. Calculate the interaction network around the hub gene.
+# Calculate the interaction network around the hub gene.
-#. Display the results in Cytoscape.
+# Display the results in Cytoscape.
-#. Select a subset of the interaction network (first-neighbors) and save it to a list.
+# Select a subset of the interaction network (first-neighbors) and save it to a list.
 ==Overview==

@@ Line 1: / Line 1: @@
 {{TutorialsTopNav}}
 ==Overview==
-The primary use of the Reverse Engineering component is to infer regulatory interactions between genes and gene products.  The Reverse Engineering component uses the information theory concept of mutual information to find these interactions.  Mutual Information is in principle more sensitive and flexible than a simple correlation calculation.  It is also invariant under data transformations, so the details of normalization should not be important.
+The primary use of the Reverse Engineering component is to infer regulatory interactions between genes and gene products.  The Reverse Engineering component uses the information theory concept of mutual information to find these interactions.  Mutual Information is in principle more sensitive and flexible than a simple correlation calculation.  It is also invariant under data transformations such as log transformation.
 The Reverse Engineering component calculates the information that the expression pattern of one gene carries about the expression of another gene, that is, it is a pairwise calculation.  Larger datasets, containing more arrays per marker, will yield greater sensitivity and better statistical support.  Full scale runs of reverse engineering algorithms, comparing all markers against each other, and typically done on datasets containing several hundred microarrays, are typically  performed on large cluster computers and are not feasible on a desktop machine.  During 2006 we hope to provide a remote service that can host such calculations for jobs launched from geWorkbench.  However, at present, smaller scale calculations are supported directly in geWorkbench.
@@ Line 18: / Line 24: @@
 #. There is not yet a tutorial for the Conditional Analysis tab.
 #. The exact algorithm used in Reverse Engineering has not been described in the literature.  The next release will implement the published ARACNE algorithm for network reverse engineering.
-#. The images shown below were calculated using a slightly larger dataset than is currently being distributed with the tutorials.  The images will be updated soon.
+#. The images shown below were calculated using a slightly larger dataset than is currently being distributed with the tutorials.  '''The images will be updated soon.'''
 ==Prerequisites==
-A dataset  containing multiple arrays (the more the better) should be loaded into geWorkbench.  If data is loaded from separate files, it should be merged into a single microarray datset, either at the time of or after being read in.  See the section  [[Tutorial - Projects and Data Files | Projects and Data Files]].  In this tutorial we will load a dataset also used in other tutorial sections, which has been normalized, and filtered to reduce the number of genes.  This file, "webmatrix_quantile_log2_dev1.2_mv0.exp" will be made available in the tutorial data section.  Its derivation is described in
+A dataset  containing multiple arrays (the more the better) should be loaded into geWorkbench.  If data is loaded from separate files, it should be merged into a single microarray datset, either at the time of or after being read in.  See the section  [[Tutorial - Projects and Data Files | Projects and Data Files]].  In this tutorial we will load a dataset also used in other tutorial sections, which has been normalized, and filtered to reduce the number of genes.  This file, "webmatrix_quantile_log2_dev1.2_mv0.exp" is available in the tutorial data section.  Its derivation is described in Tutorial_-_Data  [[Tutorial_-_Data | Data]]
 ==Example - Profiler==
 * Load the data file "webmatrix2_quantile_log2_dev1.2_mv0.exp".  This contains a set of 100 experiments on Affymetrix HG_U95Av2 chips.  As described above, this file has been quantile normalized, log2 converted, and then filtered to remove markers with a deviation of less than 1.2, in order to reduce the size of the dataset, leaving 2226 markers.

@@ Line 16: / Line 16: @@
 #. The Score vs. Probability graph does not work.  This is being fixed.
 #. There is some overlap in functionality between Profiler tab and the Conditional Analysis tab.  Both calculate aspects of conditional interactions.
 #. The exact algorithm used in Reverse Engineering has not been described in the literature.  The next release will implement the published ARACNE algorithm for network reverse engineering.
 #. The images shown below were calculated using a slightly larger dataset than is currently being distributed with the tutorials.  The images will be updated soon.