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Correlate Covariate


The Correlate Covariate command will find variables that show strong correlation to a continuous covariate (from the Design Table). To run this module, type MicroArray | Pattern | Correlate Covariate.

Correlation Covariate menu.png

Input Data Requirements

This function works on -Omic data types. The Covariate can be numeric or categorical.

General Options



  • 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.


  • 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.

Output Results

An example variable view for this command is shown below:


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.

Options are available for displaying a "Regression Line" and summary statistics as shown below:


Correlation Summary Table:



Correlate Covariate

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