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• Select the study whose data you want to analyze in the upper right portion of the caIntegrator page.
• Under Analysis Tools on the left sidebar, select K-M Plot.
• Select the For Annotation tab at the top of the page ().
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• The groups to be compared in the K-M plot originate from one patient group. Varying data sets are based upon multiple values corresponding to the selected annotation. Define Patient Groups using these options:
• Annotation Type – Select the annotation type that identifies the patient group. Selections are based on the data in the chosen study.
• Annotation – Select an annotation. Fields are based on the annotation type you select. For example, if you choose Subject, then you could select Gender or Radiation Type or any field that would distinguish the patients into groups based upon their values.
• Only annotations that are defined with permissible values display in the dropdown list.
• Values – Using conventional selection techniques, select two or more values which will be the basis for the K-M plot. Permissible (available) values or "No Values" correspond to the selected annotation.
• Survival value is the length of time the patient lived. caIntegrator displays valid survival values entered for this study. 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.

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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 gene expression data.
• Under Analysis Tools on the left sidebar, select K-M Plot.
• Select the For Gene Expression tab ().
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• For 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.

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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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See .
Gene Expression Plot for Annotation Display*
After you have defined the criteria as described in , caIntegrator generates the plot which then displays below the plot criteria.
Legends below the plot indicate the plot input. By default, the plot shows the mean of the data. displays a plot with gene expression median calculation summaries.
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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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After you have defined the criteria as described in , caIntegrator generates the plot which displays in bar graph format below the plot criteria.
By default, caIntegrator displays the mean of the data 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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Above the plot, you can select various plot types. When you do so, the plot is recalculated. Although all of the plots in this section appear similar, note the differences in calculation results and legends between the Y axis on each of the plots.
When you perform a Gene Expression simple search, by default the Gene Expression Plot () appears.
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The Gene Expression Plot () displays mean expression intensity (Geometric mean) versus Groups.
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The log2 intensity Gene Expression Plot, shown in the following figure, displays average expression intensities for the gene of interest based on Affymetrix GeneChip arrays (U133 Plus 2.0 arrays).
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The box and whisker log2 expression intensity plot displays a box plot (, ). Example box and whisker plot:uses foruses of box and whisker plots include the following:

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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="18e8c23f2a7023bb-a90e191e-4d3c4e71-86c3a911-f7ae7acdd97add3fb334f77c"><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 gene expression data.
• Click GenePattern Analysis in the left sidebar of caIntegrator. This opens the GenePattern Analysis Status page.
• In the GenePattern Analysis Status page, select Principal Component Analysis (Grid Service) from the drop down list and click New Analysis Job. This opens the Principal Component Analysis page ().
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• Select or define PCA analysis parameters, described in the following table. You must enter a job name and select an annotation query, but you can accept the other default settings..

  PCA Parameters	Description
  Job Name*	Assign a unique name to the analysis you are configuring.
  Principal Component Analysis Server*	A server which hosts the grid-enabled data GenePattern Principal Component Analysis module. Select one from the list and caIntegrator will use the selected server for this portion of the processing.
  Annotation Queries*	All annotation queries display in this list. Select one or more of these queries to define which samples are analyzed using PCA. If you select more than one query, then the union of the samples returned by the multiple queries is analyzed.
  Cluster By*	Selecting rows looks for principal components across all expression values, and selecting columns looks for principal components across all samples.
  PCA analysis options Anchor RTF39353234343a205461626c65 RTF39353234343a205461626c65

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• If you want to preprocess the data set, click Enable the Preprocess Dataset. This opens an additional set of parameters (), discussed in the following table . The preprocessing is executed prior to running the PCA.
  

  PCA Preprocessing Parameters	Description
  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.
  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
  Number of Columns Above Threshold	Remove row if n columns no >= than the given threshold
  Anchor RTF36393835383a205461626c65 RTF36393835383a205461626c65 Parameters for preprocessing data sets for PCA

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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.

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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="0cf0914bc5d3a50c-775cbd1d-429849ec-9907b994-ad39db2ac227a9767a7635ae"><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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This opens the genome site at UCSC , where you can learn more about the gene ().
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Go to the following website for a user guide for IGV: Method 2

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