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1. Select the study whose data you want to analyze in the upper right portion of the caIntegrator page. You must select a study with gene expression data.
2. Under Analysis Tools on the left sidebar, select K-M Plot.
3. Select the For Gene Expression tab, shown in the following figure. Plot criteria are described in the table following the figure.
   

   Field	Description
   Gene Symbol	Enter one or more gene symbols in the text box or click the icons to locate genes in the following databases. If you enter more than one gene in the text box, separate the entries by commas. caIntegrator provides three methods whereby you can obtain gene symbols for calculating a KM plot for gene expression. For more information, see #Choosing Genes.
   Field	Description
   Expression Type	By Fold Change: This option allows you to define in the next two fields the over- and under-expression criteria expressed in terms of fold-change. Fold change is the ratio of the measured gene expression value for an experimental sample to the expression value for the control sample. By Expression Level: This option allows you to run a KM gene expression plot when there is no control group nor reference data set. In the next two fields, enter values Overexpressed (above expression level) or Underexpressed (below expression level).
   Survival Value	This fieldSurvival value, which represents the length of time the patient lived, is required for both expression types. Select the survival measure which is the unit of measurement for the survival value to be used for the plot.
   Control Sample Sets	This field is required only for fold change data. One or more control sets are created by the study manager when a study is deployed. Select the Control Sample Set you would like to use to calculate fold-change. Info title Note If the study has more than one platform associated with it, the platform is inherently selected when you select the control set. Control sets are comprised of samples from only one platform.

   #Click
4. Click the Create Plot button.

caIntegrator generates the plot which then displays below the plot criteria.An example displays in the following figure.

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1. Select the study whose data you want to analyze in the upper right portion of the caIntegrator page. You must select a study saved as a subject annotation study, but which has genomic data.
2. Click GenePattern Analysis in the left sidebar of caIntegrator. This opens the GenePattern Analysis Status page.
3. In the GenePattern Analysis Status page, select Comparative Marker Selection (Grid Service) from the drop down list and click New Analysis Job. This opens the Comparative Marker Selection Analysis page, shown in the following figure.
   
4. Select or define CMS analysis parameters, described in the following table. An asterisk indicates required fields. The default settings are valid; they should provide valid results.

   CMS Parameter	Description
   Job Name*	Assign a unique name to the analysis you are configuring.
   Preprocess Server*	A server which hosts the grid-enabled data GenePattern PreProcess Dataset module. Select one from the list and caIntegrator will use the selected server for this portion of the processing.
   Comparative Server*	A server which hosts the grid-enabled data GenePattern Comparative Marker Selection module. Select one from the list and caIntegrator will use the selected server for this portion of the processing.
   Annotation Queries and Lists*	All subject annotation queries and gene lists with appropriate data for the analysis are listed. Select and move two or more queries from the All Available Queries panel to the Selected Queries panel using the Add > and Remove < buttons. <ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="5ec39accdfb4afbd-cc7cff17-496449da-af3f84f6-920860a04778e5b88500147f"><ac:plain-text-body><![CDATA[Note: The [SL] and [Q] prefixes to list names indicate "Subject Lists" or "Saved Queries". A "G" in the prefix indicates the list is Global. For more information, see [Creating a Gene or Subject List	https://wiki.nci.nih.gov/x/FoDnAg#4-ViewingQueryResults-CreatingaGeneorSubjectList].	]]></ac:plain-text-body></ac:structured-macro>
   Filter Flag	Variation filter and thresholding flag
   Preprocessing Flag*	Discretization and normalization flag
   Min Change*	Minimum fold change for filter
   Min Delta*	Minimum delta for filter
   Threshold*	Value for threshold
   Ceiling*	Value for ceiling
   Max Sigma Binning*	Maximum sigma for binning
   Probability Threshold*	Value for uniform probability threshold filter
   Num Exclude*	Number of experiments to exclude (max & min) before applying variation filter
   Log Base Two	Whether to take the log base two after thresholding; default setting is "Yes".
   Number of Columns Above Threshold*	Remove row if n columns are not >= than the given threshold In other words, the module can remove rows in which the given number of columns does not contain a value greater or equal to a user defined threshold.
   Test Direction*	The test to perform (up-regulated for class0; up-regulated for class1, two sided). By default, Comparative Marker Selection performs the two-sided test.
   Test Statistic*	Select the statistic to use.
   Min Std*	The minimum standard deviation if test statistic includes the min std option. Used only if test statistic includes the min std option.
   Number of Permutations*	The number of permutations to perform. (Use 0 to calculate asymptotic P-values.) The number of permutations you specify depends on the number of hypotheses being tested and the significance level that you want to achieve (3). The greater the number of permutations, the more accurate the P-value. Complete – Perform all possible permutations. By default, complete is set to No and Number of Permutations determines the number of permutations performed. If you have a small number of samples, you might want to perform all possible permutations. Balanced – Perform balanced permutations
   Random Seed*	The seed for the random number generator.
   Smooth P-values	Whether to smooth P-values by using the Laplace's Rule of Succession. By default, Smooth P-values is set to Yes, which means P-values are always less than 1.0 and greater than 0.0.
   Phenotype Test*	Tests to perform when class membership has more than 2 classes: one versus-all, all pairs. Note: The P-values obtained from the one-versus-all comparison are not fully corrected for multiple hypothesis testing.
   Comparative Marker Selection analysis options Anchor RTF31353237303a205461626c65 RTF31353237303a205461626c65

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1. Select the study whose data you want to analyze in the upper right portion of the caIntegrator page. You must select a study with copy number (either Affymetrix SNP or Agilent Copy Number) data.
2. Click GenePattern Analysis in the left sidebar of caIntegrator. This opens the GenePattern Analysis Status page.
3. In the GenePattern Analysis Status page, select GISTIC (Grid Service) from the drop down list and click New Analysis Job. This opens the GISTIC Analysis page, shown in the following figure.
   
4. Select or define GISTIC analysis parameters, as described in the following table. You must indicate a Job Name, but you can accept the other defaults settings, which are valid and should produce valid results.

   GISTIC Parameters	Description
   Job Name*	Assign a unique name to the analysis you are configuring.
   GISTIC Service Type*	Select whether to use the GISTIC web service or grid service and provide or select the service address. If the web service is selected, authentication information is also required
   GenePattern User Name/Password	Include these to log into GenePattern for the analysis.
   Annotation Queries and Lists	All annotation queries display in this list as well as an option to select all non-control samples. Select an annotation query if you wish to run GISTIC on a subset of the data and select all non-control samples if wish to include all samples.
   Select Platform	This option appears only if more than one copy number platform exists in the study. Select the appropriate platform from the drop-down list ().
   Exclude Sample Control Set	From the drop-down list, select the name of the control set you want to exclude from the analysis. Click None if that is applicable.
   Amplifications Threshold*	Threshold for copy number amplifications. Regions with a log2 ratio above this value are considered amplified. Default = 0.1.
   Deletions Threshold*	Threshold for copy number deletions. Regions with a log2 ratio below the negative of this value are considered deletions. Default = 0.1.
   Join Segment Size*	Smallest number of markers to allow in segments from the segmented data. Segments that contain fewer than this number of markers are joined to the neighboring segment that is closest in copy number. Default = 4.
   <ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="dd8422084e99d754-c8a2573e-483f4cd4-a5229929-33ceb1790a699273ec0484eb"><ac:plain-text-body><![CDATA[	QV Thresh[hold]*	Threshold for q-values. Regions with q-values below this number are considered significant. Default = 0.25.	]]></ac:plain-text-body></ac:structured-macro>
   Remove X*	Flag indicating whether to remove data from the X-chromosome before analysis. Allowed values = {1,0}. Default = 1(yes).
   cnv File	This selection is optional. Browse for the file. There are two options for the CNV file. Option #1 enables you to identify CNVs by marker name. Permissible file format is described as follows: A two column, tab-delimited file with an optional header row. The marker names given in this file must match the marker names given in the markers_file. The CNV identifiers are for user use and can be arbitrary. The column headers are:

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