DFP (version 1.30.0)

DFP-package: DFP Package Overview

Description

This package provides a supervised technique able to identify differentially expressed genes, based on the construction of Fuzzy Patterns (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values.

Details

Package:

DFP

Type:

Package

Version:

1.0

Date:

2008-07-03

License:

GPL-2

The main functionality of the package is provided by the discriminantFuzzyPattern function, which works in a 4-step process:

1. Calculates the Membership Functions. These functions are used in the next step to discretize gene expression data.
2. Discretizes the gene expression data (float values) into ‘Low’, ‘Medium’ or ‘High’ labels.
3. Calculates a Fuzzy Pattern for each category. To do this, a given percentage of the samples belonging to a category must have the same label (‘Low’, ‘Medium’ or ‘High’).
4. Calculates the Discriminant Fuzzy Pattern (DFP) that includes those genes present in two or more FPs with different assigned labels.

Additional data classes: ExpressionSet, AnnotatedDataFrame.

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

Examples

#########################################
############ Get sample data ############
#########################################
library(DFP)
data(rmadataset)

#########################################
# Filter the most representative genes  #
#########################################
res <- discriminantFuzzyPattern(rmadataset)

#########################################
###### Different result displays ########
#########################################
plotMembershipFunctions(rmadataset, res$membership.functions, featureNames(rmadataset)[1:2])
showDiscreteValues(res$discrete.values, featureNames(rmadataset)[1:10], c("healthy", "AML-inv"))
showFuzzyPatterns(res$fuzzy.patterns, "healthy")[21:50]
plotDiscriminantFuzzyPattern(res$discriminant.fuzzy.pattern)