discretizeExpressionValues: Function to discretize gene expression data

Description

Discretizes the gene expression data (float values) into Low, Medium or High labels.

Usage

discretizeExpressionValues(rmadataset, mfs, zeta = 0.5, overlapping = 2)

Arguments

rmadataset

ExpressionSet with numeric values containing gene expression values (rows) of samples belonging to different categories (columns). The ExpressionSet also contains an AnnotatedDataFrame with metadata regarding the classes to which each sample belongs.

mfs

Membership functions to determine the discret value (linguistic label) corresponding to a given gene expression level.

zeta

Threshold value which controls the activation of a linguistic label ('Low', 'Medium' or 'High'). The lower, the less posibilities of having genes with more than one assigned linguistic label. Default value = 0.5. Range[0,1].

overlapping

Modifies the number of membership functions used in the discretization process. Possible values:

Low,Medium,High.
Low,Low-Medium,Medium,Medium-High,High.
Low,Low-Medium,Low-Medium-High,Medium,Medium-High,High.

Default value = 2.

Value

Matrix containing discrete values according to the overlapping parameter after discretizing the gene expression values. Includes an attribute types which determines the category of each sample.

References

F. Diaz; F. Fdez-Riverola; D. Glez-Pena; J.M. Corchado. Using Fuzzy Patterns for Gene Selection and Data Reduction on Microarray Data. 7th International Conference on Intelligent Data Engineering and Automated Learning: IDEAL 2006, (2006) pp. 1095-1102