CNStatus table correlation

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CNStatus Correlate Covariate


To calculate the correlation between segment-level or gene-level CNStatus data to a sample covariate from the design table, we can use the Correlate Covariate command. To run this analysis, convert the CNStatus table into a MicroArray table, and then go to OmicData | Pattern | Correlate Covariate.

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Input Data Requirements

This function works on -Omic data types. For categorical covariates, FTest or KruskallWallis correlation must be used.

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Convert CNStatus table to -Omic Data

Open the segmentLevel.CNStatus table, then click either the txt icon or the excel icon to open the table in your computer's table viewer.


Save the table to your computer, then re-import the CNstatus data as a generic table, by choosing Add Data -> Add Table Data -> Add Table From File


Right click the table added, choose Convert to MicroArray Data:


Right click the Design folder under the new -Omic Data, to import a new design table (contains your sample covariates):


A new design table has been imported.


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Correlate Covariate

Go to OmicData -> Pattern -> Correlate Covariate

Correlation Covariate menu.png

Run KruskalWallis test for categorical data, or the appropriate test for continuous data.


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  • Project & Data: The window includes a dropdown box to select the Project and Data object to be filtered.
  • Variables: Selections can be made on which variables should be included in the filtering (options include All variables, Selected variables, Visible variables, and Customized variables (select any pre-generated Lists)).
  • Observations: Selections can be made on which observations should be included in the filtering (options include All observations, Selected observations, Visible observations, and Customized observations (select any pre-generated Lists).
  • Output name: The user can choose to name the output data object.

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  • Correlate with: The user can choose which column of the Design Table to run correlations against (This could be a numeric column, i.e. a continuous covariate, or a categorical column, i.e. gender).
  • Correlation method: Define the method to calculate correlation. Available options are Pearson, Kendell, Spearman, FTest, and KruskalWallis (see
    • For categorical, users can choose either FTest or KruskalWallis test.
    • For numerical, users can choose Person, Kendel, or Spearman test.
  • Multiplicity: The user can specify the type of Multiplicity test (None, FDR_BH, FDR_BY, Bonferroni, Sidak, StepDownBonferroni, StepDownSidak, and StepUp--with BDR_BH being the default option)
  • Fixed neighbor number: When selected, Array Studio will return a user-specified number of top-ranked variables.
  • With p-value<: When selected, Array Studio will output variables with a p-value smaller than specified value.
  • Generate correlation view for each covariate:If the user selects multiple covariate factors in the design table, selecting this option will generate correlation plot as show below for each factor.
  • Generate summary report: Selecting this option will generate a summary table.
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Output Results

An example variable view for this command is shown below: Explore the correlation plots


For each identified gene or probeset, the samples will be plotted along the X-axis for the specified numeric covariate, and expression will be plotted on the Y-axis.

Correlation Summary Table:


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  Begin CorrelateCovariate /Namespace=MicroArray;
  Project CNV_server;
  Data CNV_server\\segmentLevelData_Micro_CNStatus;
  Target Sex;
  Options /CorrelationType=KruskalWallis /NeighborNumber=5836 /Multiplicity=FDR_BH /AlphaLevel=0.01 /GenerateCorrelationView=True /GenerateSummaryReport=False;
  Output outputName;
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