discretizeExpressionValues: Function to discretize gene expression data
In DFP: Gene Selection

Description Usage Arguments Value Author(s) References

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

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

`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`.

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.

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <rodrigo.djv@uvigo.es>

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

DFP documentation built on Nov. 8, 2020, 7:46 p.m.