homeabout uscontact us





Discretization is the process of converting real gene expression data into a typically small number of finite values (e.g. high, medium, low). The variation in the original data is maintained in the discretized dataset. Discretization is a necessary precursor to using association mining algorithms such as SLAMô to find associations.

Discretization is accomplished by assigning each value in a dataset to a bin. The data ranges (bin boundaries) and number of bins are set on the Discretization parameters dialog.


Quantile Discretization


Range Discretization


Discretization Target

Discretization can be based on the genes, samples or all of the data in a dataset.



1. Click a dataset in the Experiments navigator. The item is highlighted.

2. Click the Discretize Data toolbar icon , or select Discretize Data from the Predict menu, or right-click the item and select Discretize Data from the shortcut menu. The Discretization dialog is displayed.

3. Set the parameters.




Type of discretization: Quantile or Range.


Discretize Per Gene, Per Sample or All Data.

Number of Bins

The number of discrete groups (bins) to put the values into.


4. Click OK. The Experiment Progress dialog is displayed. It is dynamically updated as the Discretization operation is performed. To cancel the Discretization operation, click the Cancel button.

Upon successful completion, a new dataset is added under the original dataset in the Experiments navigator.


Related Topics:

ANN Classification and Prediction Overview