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• Select the study whose data you want to analyze in the upper right portion of the caIntegrator page. The queries you identify for the K-M plot must have been saved previously in caIntegrator.
• Under Analysis Tools on the left sidebar, select K-M Plot.
• Select the For Queries and Saved Lists tab ().
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• Queries – Select Queries whose data you want to analyze from the All Available Queries panel and move them to the Selected Queries panel using the Add >> button.
• Genomic queries do not appear in the lists; they cannot be selected for this type of K-M plot.
• Exclusive Subject in Queries – Check the box if you want to exclude any subjects that appear in both (or all) queries selected for the plot, thus eliminating overlap.
• Add Additional Group...all other subjects – Check the box to create an additional group of all other subjects that are not in selected query groups.
• Survival value – The length of time the patient lived. Select the survival measure which is the unit of measurement for the survival value to be used for the plot.
• Click the Create Plot button. caIntegrator generates the plot which then displays below the plot criteria ().

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• Select the study whose data you want to analyze in the upper right portion of the caIntegrator page. (You must select a study which has genomic data.)
• Under Analysis Tools on the left sidebar, select Gene Expression Plot.
• Select the For Genomic Queries tab ().
  
• Genomic Query – Click on the genomic query upon which the plot is to be based.
• Reporter Type – Select the radio button that describes the reporter type:
• Reporter ID – Summarizes expression levels for all reporters you specify.
• Gene Name – Summarizes expression levels at the gene level.
• Click the Create Plot button. caIntegrator generates the plot which then displays below the plot criteria. Legends below the plot indicate the plot input ().
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• You can recalculate the data display by clicking the Plot Type above the graph. See .
• You can modify the plot parameters and click the Reset button to recalculate the plot.

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• Indicate whether a distribution is skewed and whether there are potential unusual observations (outliers) in the data set.
• Perform a large number of observations.
• Compare two or more data sets.
• Compare distributions because the center, spread, and overall range are immediately apparent.
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In descriptive statistics, a box plot or boxplot, also known as a box-and-whisker diagram or plot, is a convenient way of graphically depicting groups of numerical data through their five-number summaries (the smallest observation excluding outliers, lower quartile \[Q1\], median \[Q2\], upper quartile \[Q3\], and largest observation excluding outliers).

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• 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.
• Click GenePattern Analysis in the left sidebar of caIntegrator. This opens the GenePattern Analysis Status page.
• 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 ().
  
• 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="be67e166877768c1-143ac3d7-4eec42fa-946285b0-99ac2ccd39859c67758775b8"><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 on page 69.	]]></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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• When you have completed the form, click Perform Analysis.

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• 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.
• Click GenePattern Analysis in the left sidebar of caIntegrator. This opens the GenePattern Analysis Status page.
• 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 ().
  
• 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="4967525db1fb7878-3d899deb-494243e1-83cca64e-010984eb88cdf6bdafe33487"><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:

• Marker Name
  
• CNV Identifier
  
  Option #2 enables you to identify CNVs by genomic location. Permissible file format is described as follows:
  A 6 column, tab-delimited file with an optional header row. The 'CNV Identifier', 'Narrow Region Start' and 'Narrow Region End' are for user use and can be arbitrary. The column headers are:
  
• CNV Identifier
  
• Chromosome
  
• Narrow Region Start
  
• Narrow Region End
  
• Wide Region Start
  
• Wide Region End|

  GISTIC analysis parameters

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• When you have completed the form, click Perform Analysis.
• When the job is complete, the system displays a completion date on the GenePattern Analysis status page. Click the Download link. This downloads zipped result files to your local work station. The number of files and their file type will vary according to the processing. The results format is compatible with GenePattern visualizers and can be uploaded within GenePattern.
• Additionally, upon completion of a successful GISTIC anaylsis, caIntegrator automatically displays the two gene lists that it generates in the Gene List Picker so that you can use them in a caIntegrator query or plot calculation. The lists are visible only to your userID. For more information, see . The genes will also display in Saved Copy Number Analyses in the left sidebar. See on page 74.
• If samples from a copy number source are deleted, the GISTIC job in which they are appear is also deleted.

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