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Categorical Trait Association


The Categorical Trait Association function will generate a report table, containing p-values and other genotype analysis information (specified under Options), for a study containing a categorical trait. This includes binary (case-control traits) as well multi-category traits (categorical traits). For binary traits, this can be used instead of the Basic Association test, but allows greater flexibility. This function also allows the user to specify if a categorical trait is ordered, and run an analysis based on that information.

To run this module, type MicroArray | Inference | Other Tests | Microarray Categorical Trait Association

Microarray Categorical Traits menu.png

Input Data Requirements

This function works on -Omic data types.

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.


Specify Model


  • Trait: The user should specify a categorical factor from the design table. The user interface will automatically update the box to the right to reflect the type of Trait specified. It is required that this be a Categorical trait or Binary Trait for this type of analysis.
  • Levels are ordered: For an ordinal analysis, the user should check this box to indicate that the selected factor is ordered.
  • Columns: This section contains columns from the Data object's Design Table.
    • Class: If the column should be considered a Class term, a checkbox for that column can be selected. By default, Array Studio will guess on what constitutes a Class term. In general, numeric columns will not be considered Class terms by default, while other column, such as "Factors", will be considered Class terms by default. Users should consult with a statistician if not sure as to whether a column should be a class term. In the example shown below, time should be considered a Class term, but because Array Studio made it a numeric column, it is not by default. Changing this in the Design Table will affect the default behavior here. Here, the VARIABLE (e.g., the gene expression) is selected by default. Other factors from the design table can be further added into the model as co-variate.
    • Term: the factors in the design table.
  • Construct Model:this section is where the user can add the terms to the model. By selecting terms on the left, the user can use the Add, Cross, Nest, and Remove buttons to select the terms for that particular model.
    • Add: Clicking this button will add the selected terms to the model.
    • Cross: Clicking this button will cross the terms selected on the left (this is discussed in more detail later).
    • Nest: Clicking this button will nest the selected term on the left panel to the selected term on the right panel.
    • Remove: Clicking this button will remove selected terms in right panel.
  • Report p-value for covariate terms: output the p-values for covariate terms included in the model.
  • Report goodness of fit test p-value:Report "goodness of fit" test p-value.
  • Generate odds ratio data:Report odds ratio data between different levels of the traits.
    • Append to report: Append the ratio data to the report for each variable.
  • Multiplicity: The user can specify a Multiplicity test (None, FDR_BH, FDR_BY, Bonferroni, Sidak, StepDownBonferroni, StepDownSidak, and StepUp--with BDR_BH being the default option)
  • ANOVA test type: Type1, Type2, Type3, and Type4 (Type 3 is the default option) - These are sum of square types that are related to ANOVA. They only make a difference if you have an unbalanced design. Type 1, 2 and 3 are universally accepted types, and Type 4 is SAS specific. Type 3 is mostly commonly used and generally correct. For additional information please see the following link: http://en.wikipedia.org/wiki/Explained_sum_of_squares.
  • Test Type: Specify the way to perform the hypothesis test(options include WaldTest and LikelihoodRatioTest).
  • Alpha level: The p-value cutoff to be used for the generation of the List.
  • Confidence interval: Specify the parameter for confidence interval.

Output Results

An example Categorical Trait Association report is shown below, which contains raw and adjusted p-values for each variable:

Categorical Traits result.png


Categorical Trait Association

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