PCA - Revision history

Smith: /* Requirements */

2015-02-11T17:03:06Z

‎Requirements

Smith: /* Requirements */

2015-02-11T17:01:36Z

‎Requirements

Smith: /* Principle Component Analysis (PCA) */

2015-01-27T22:09:02Z

‎Principle Component Analysis (PCA)

Smith: /* Principle Component Analysis (PCA) */

2015-01-27T22:08:44Z

‎Principle Component Analysis (PCA)

Smith at 19:07, 22 April 2014

2014-04-22T19:07:26Z

Smith: /* Principle Component Analysis (PCA) */

2014-01-24T20:41:24Z

‎Principle Component Analysis (PCA)

Smith: /* References - PCA */

2014-01-24T20:32:58Z

‎References - PCA

Smith: /* References */

2014-01-24T20:32:49Z

‎References

Smith: /* GenePattern Server Settings */

2014-01-14T21:58:55Z

‎GenePattern Server Settings

Smith: /* GenePattern Server Settings */

2014-01-14T21:46:33Z

‎GenePattern Server Settings

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 ==Requirements==
-The PCA 3D viewer has special requirements.  The computer display driver must support OpenGL version 1.2 or higher.  If it does not, a warning message will be displayed at the time the user attempts to create a 3D plot.  Any recent graphics card should provide this support.
+The PCA 3D viewer requires that the computer display driver support OpenGL version 1.2 or higher.  If it does not, a warning message will be displayed at the time the user attempts to create a 3D plot.  Any recent graphics card should provide this support.
 ==Parameters==

@@ Line 19: / Line 19: @@
 The PCA 3D viewer has special requirements.  The computer display driver must support OpenGL version 1.2 or higher.  If it does not, a warning message will be displayed at the time the user attempts to create a 3D plot.  Any recent graphics card should provide this support.
-The 3D visualization does not currently work when using 64-bit Java 7 on Mac OSX.
+The 3D visualization does not currently work when using 64-bit Java, which is now the default on all platforms.  It will work if a 32-bit version of Java is used to run geWorkbench (not available for Mac OS X).
 ==Parameters==

← Older revision		Revision as of 22:09, 27 January 2015
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	The analysis can be done either in terms of experiments (arrays) or genes. The result will be the most important features of the experiments or genes in explaining the data.		The analysis can be done either in terms of experiments (arrays) or genes. The result will be the most important features of the experiments or genes in explaining the data.

−	Note - due to memory limitations, analysis on a full set of genes may not be possible. Instead, you may need to filter the dataset or define a [[Marker_Sets \| Marker Set]] with a relevant subset of genes.	+	'''Note''' - due to memory limitations, analysis on a full set of genes may not be possible. Instead, you may need to filter the dataset or define a [[Marker_Sets \| Marker Set]] with a relevant subset of genes.

← Older revision		Revision as of 22:08, 27 January 2015
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	The analysis can be done either in terms of experiments (arrays) or genes. The result will be the most important features of the experiments or genes in explaining the data.		The analysis can be done either in terms of experiments (arrays) or genes. The result will be the most important features of the experiments or genes in explaining the data.

−	Note - due to memory limitations, analysis on a full set of genes may not be possible.	+	Note - due to memory limitations, analysis on a full set of genes may not be possible. Instead, you may need to filter the dataset or define a [[Marker_Sets \| Marker Set]] with a relevant subset of genes.

@@ Line 75: / Line 75: @@
 After running an analysis, any number of components can be graphed as to the weight of the contribution from each original variable by highlighting the desired components.  Each principle component is shown as a line with a different color.  In the table below the graph, the individual weights defining each principle component are shown.
-The data displayed in the table can be copied to a new top-level dataset in the workspace by pushing the "Create MA Set" button.
+The data displayed in the table can be copied to a new top-level dataset in the [[Workspace]] by pushing the "Create MA Set" button.
@@ Line 94: / Line 94: @@
 ====Create MA Set====
-This button will copy the principal components being displayed in the tabular view to a new data matrix, which is then placed in the Workspace as a new, top-level "microarray set" (hence "Create MA Set").  Here, the matrix of principle components can be further displayed and analyzed, for example using the Tabuluar Microarray Viewer, the Scatter Plot, or other appropriate components.
+This button will copy the principal components being displayed in the tabular view to a new data matrix, which is then placed in the [[Workspace]] as a new, top-level "microarray set" (hence "Create MA Set").  Here, the matrix of principle components can be further displayed and analyzed, for example using the Tabuluar Microarray Viewer, the Scatter Plot, or other appropriate components.
 If the initial analysis variable was "genes", the items displayed e.g. in the Tabular Microarray Viewer will be the experiments, that is, microarrays.  Their names will appear in the Tabular Microarray Viewer column labeled "Markers", as this labeling is currently fixed.
@@ Line 121: / Line 121: @@
 * '''Plot''' - Draw a plot for the selected components (2 or 3).
 * '''Clear Plot''' - Erase the current plot.
-* '''Image Snapshot''' - save a snapshot of the projection to the Workspace.
+* '''Image Snapshot''' - save a snapshot of the projection to the [[Workspace]].
 ====2D Projection====
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-Below, the PCA result node is shown in the Workspace, along with a highlighted gene.
+Below, the PCA result node is shown in the [[Workspace]], along with a highlighted gene.

@@ Line 6: / Line 6: @@
 [https://en.wikipedia.org/wiki/Principal_component_analysis Principle Component Analysis] (Raychaudhuri et al., 2000) is used to find the most important contributors to the variance in a dataset.
-geWorkbench can dispatch a PCA job to a GenePattern server, and display the returned result.
+geWorkbench can dispatch a PCA job to a [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] server, and display the returned result.
 The analysis can be done either in terms of experiments (arrays) or genes. The result will be the most important features of the experiments or genes in explaining the data.
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 For further information on the GenePattern implementation of PCA, please see the [http://www.broadinstitute.org/cancer/software/genepattern GenePattern] documentation at [http://www.broadinstitute.org/cancer/software/genepattern/modules GenePattern Analysis:Modules].
 ==Requirements==

← Older revision		Revision as of 20:32, 24 January 2014
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	==References - PCA==		==References - PCA==
	* Raychaudhuri S, Stuart JM, and Altman RB. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455-466. [http://www.ncbi.nlm.nih.gov/pubmed/10902193 PubMed ID 10902193].		* Raychaudhuri S, Stuart JM, and Altman RB. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455-466. [http://www.ncbi.nlm.nih.gov/pubmed/10902193 PubMed ID 10902193].
−
−	* [http://www.broadinstitute.org/cancer/software/genepattern/modules GenePattern Modules]

@@ Line 167: / Line 167: @@
 [[Image:PCA_variable_genes_with_array_set.png]]
-==References==
+==References - GenePattern==
 * Raychaudhuri S, Stuart JM, and Altman RB. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455-466.  [http://www.ncbi.nlm.nih.gov/pubmed/10902193 PubMed ID 10902193].
 * [http://www.broadinstitute.org/cancer/software/genepattern/modules GenePattern Modules]

@@ Line 50: / Line 50: @@
-* '''Protocol''' - http
+To run GenePattern components, a GenePattern account is required.
-* '''Host''' - enter the URL of an available GenePattern server.
-* '''Port''' - Enter the port number of the GenePattern server you are using.
+Pushing "'''Modify'''" brings up an editing box where any of the settings can be changed.
-* '''Username''' - enter a GenePattern username
-* '''Password''' - the GenePattern password for username, if required.
+* '''Protocol''' - HTTP or HTTPS, depending on the server being used.
 ==Results Viewer==

@@ Line 44: / Line 44: @@
 ===GenePattern Server Settings===
-[[Image:PCA_GenePattern_Server_Settings.png]]
+You can connect to any running GenePattern server to run the analysis (provided it has the required module installed).  An example configuration of the "GenePattern Server Settings" tab is shown here:
 * Protocol - http
-* Host - enter the URL of an available GenePattern server.
+[[Image:GP_Server_Settings.png]]
 * Port - Enter the port number of the GenePattern server you are using.
 * Username - enter a GenePattern username
-* Password - the GenePattern password for username, if required.
+* '''Protocol''' - http
 ==Results Viewer==