Package 'DFP'

Title: Gene Selection
Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values.
Authors: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola
Maintainer: Rodrigo Alvarez-Glez <[email protected]>
License: GPL-2
Version: 1.63.0
Built: 2024-10-01 04:37:07 UTC
Source: https://github.com/bioc/DFP

Help Index

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.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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)

Calculates the Discriminant Fuzzy Pattern to select significative genes

Description

Calculates the Discriminant Fuzzy Pattern (DFP) that includes those genes present in two or more FPs with different assigned labels.

Usage

calculateDiscriminantFuzzyPattern(rmadataset, fps)

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.
fps

Genes belonging to each Fuzzy Patterns. There are one FP for each class.
Includes an attribute ifs with the Impact Factor for each category.

Value

Genes belonging to the final DFP.
Includes an attribute ifs with the Impact Factor for each category.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

Calculates a Fuzzy Pattern for each category of the samples

Description

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’).

Usage

calculateFuzzyPatterns(rmadataset, dvs, piVal = 0.9, 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.
dvs

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.
piVal

Controls the degree of exigency for selecting a gene as a member of a Fuzzy Pattern.
Default value = 0.9. Range[0,1].

overlapping

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

  1. ‘Low’, ‘Medium’, ‘High’.

  2. ‘Low’, ‘Low-Medium’, ‘Medium’, ‘Medium-High’, ‘High’.

  3. ‘Low’, ‘Low-Medium’, ‘Low-Medium-High’, ‘Medium’, ‘Medium-High’, ‘High’.

Default value = 2.

Value

Genes belonging to each Fuzzy Patterns. There are one FP for each class.
Includes an attribute ifs with the Impact Factor for each category.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

Calculates Membership Functions

Description

Calculates the Membership Functions. These functions are used in the next step (discretizeExpressionValues) to discretize gene expression data.

Usage

calculateMembershipFunctions(rmadataset, skipFactor = 3)

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.
skipFactor

Numeric value to omit odd values (a way of normalization).
Higher values imply that less samples of a gene are considered as odd. If skipFactor=0 do NOT skip.
Default value = 3. Range[0,).

Value

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

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

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:

  1. ‘Low’, ‘Medium’, ‘High’.

  2. ‘Low’, ‘Low-Medium’, ‘Medium’, ‘Medium-High’, ‘High’.

  3. ‘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.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

Discriminant Fuzzy Pattern to filter genes

Description

discriminantFuzzyPattern discovers significant genes based on the construction of Fuzzy Patterns (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to the gene expression values in the matrix rmadataset.

Usage

discriminantFuzzyPattern(rmadataset, skipFactor = 3, zeta = 0.5, overlapping = 2, piVal = 0.9)

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.
skipFactor

Numeric value to omit odd values (a way of normalization).
Higher values imply that less samples of a gene are considered as odd. If skipFactor=0 do NOT skip.
Default value = 3. Range[0,).

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:

  1. ‘Low’, ‘Medium’, ‘High’.

  2. ‘Low’, ‘Low-Medium’, ‘Medium’, ‘Medium-High’, ‘High’.

  3. ‘Low’, ‘Low-Medium’, ‘Low-Medium-High’, ‘Medium’, ‘Medium-High’, ‘High’.

Default value = 2.

piVal

Controls the degree of exigency for selecting a gene as a member of a Fuzzy Pattern.
Default value = 0.9. Range[0,1].

Details

The discriminantFuzzyPattern function 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.

Value

membership.functions

Membership functions to determine the discret value corresponding to a given gene expression level.

discrete.values

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

fuzzy.patterns

Genes belonging to each Fuzzy Patterns. There are one FP for each class.
Includes an attribute ifs with the Impact Factor for each category.

discriminant.fuzzy.pattern

Genes belonging to the final DFP.
Includes an attribute ifs with the Impact Factor for each category.

params

The parameters used to tune the algorithm (as arguments in the function).

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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)

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

Class "ExpressionLevel"

Description

A virtual class which represents a generic Membership Function.

Objects from the Class

A virtual Class: No objects may be created from it.

Slots

center:

Object of class "numeric". Represents the peak point in the function curve.

width:

Object of class "numeric". Represents the length of values lower than 1 and greater than 0 in the function curve.

Methods

show

signature(object = "ExpressionLevel"): Prints the ExpressionLevel subclass of the object.

setValues

signature(object = "ExpressionLevel", values = "numeric"): Generic function to be implemented in the subclasses.

computeMembership

signature(object = "ExpressionLevel", x = "numeric"): Generic function to be implemented in the subclasses.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

showClass("ExpressionLevel")

Class "HighExpressionLevel"

Description

A class which represents a Membership Function to determine the membership of a numeric value to the ‘High’ discrete label. The result depends on the ‘center’ and ‘width’ values.

Objects from the Class

Objects can be created by calls of the form new("HighExpressionLevel").

Slots

center:

Object of class "numeric". Represents the peak point in the function curve.

width:

Object of class "numeric". Represents the length of values lower than 1 and greater than 0 in the function curve.

Extends

Class "ExpressionLevel", directly.

Methods

setValues

signature(object = "HighExpressionLevel", values = "numeric"): Establishes the ‘center’ and ‘width’ slots of the object, given a vector of numeric values.

computeMembership

signature(object = "HighExpressionLevel", x = "numeric"): Returns a value in the [0,1] interval, which represents the membership to the ‘High’ discrete label.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

showClass("HighExpressionLevel")

Class "LowExpressionLevel"

Description

A class which represents a Membership Function to determine the membership of a numeric value to the ‘Low’ discrete label. The result depends on the ‘center’ and ‘width’ values.

Objects from the Class

Objects can be created by calls of the form new("LowExpressionLevel").

Slots

center:

Object of class "numeric". Represents the peak point in the function curve.

width:

Object of class "numeric". Represents the length of values lower than 1 and greater than 0 in the function curve.

Extends

Class "ExpressionLevel", directly.

Methods

setValues

signature(object = "LowExpressionLevel", values = "numeric"): Establishes the ‘center’ and ‘width’ slots of the object, given a vector of numeric values.

computeMembership

signature(object = "LowExpressionLevel", x = "numeric"): Returns a value in the [0,1] interval, which represents the membership to the ‘Low’ discrete label.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

showClass("LowExpressionLevel")

Class "MediumExpressionLevel"

Description

A class which represents a Membership Function to determine the membership of a numeric value to the ‘Medium’ discrete label. The result depends on the ‘center’ and ‘width’ values.

Objects from the Class

Objects can be created by calls of the form new("MediumExpressionLevel").

Slots

center:

Object of class "numeric". Represents the peak point in the function curve.

width:

Object of class "numeric". Represents the length of values lower than 1 and greater than 0 in the function curve.

Extends

Class "ExpressionLevel", directly.

Methods

setValues

signature(object = "MediumExpressionLevel", values = "numeric"): Establishes the ‘center’ and ‘width’ slots of the object, given a vector of numeric values.

computeMembership

signature(object = "MediumExpressionLevel", x = "numeric"): Returns a value in the [0,1] interval, which represents the membership to the ‘Medium’ discrete label.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

showClass("MediumExpressionLevel")

Plots the Discriminant Fuzzy Pattern of the relevant genes

Description

This function plots the Discriminant Fuzzy Pattern of the relevant genes (in rows) for the sample classes (in columns), as well as the impact factor which determines if a gene belongs to a Fuzzy Pattern in a class (if its value is higher than the piVal).
The relevant genes are those which are present in almost two different Fuzzy Patterns with different linguistic labels.
The plotting is made in both graphical and text mode.

Usage

plotDiscriminantFuzzyPattern(dfp, overlapping = 2)

Arguments

dfp

A matrix with the fuzzy patterns and impact factors for the relevant genes.

overlapping

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

  1. ‘Low’, ‘Medium’, ‘High’.

  2. ‘Low’, ‘Low-Medium’, ‘Medium’, ‘Medium-High’, ‘High’.

  3. ‘Low’, ‘Low-Medium’, ‘Low-Medium-High’, ‘Medium’, ‘Medium-High’, ‘High’.

Default value = 2.

Value

A matrix with the discriminant genes in rows, along with the Fuzzy Pattern for each class (in columns).
This object contains an attribute (ifs) which stores the Impact Factors used to determine if a gene belongs to a Fuzzy Pattern in a class (if the value is higher than the piVal).

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

Plots the Membership Functions (Low, Medium, High) used to discretize gene expression values

Description

Each gene has 3 Membership Functions (‘Low’, ‘Medium’ and ‘High’) which can be plotted as curves in graphical mode.
In the text mode a membership function is represented with its center and width.
This function receives one or more gene names and plots the results in both graphical and text mode. If a set of genes containing more than 36 elements is provided, only the text mode is available.

Usage

plotMembershipFunctions(rmadataset, mfs, genes)

Arguments

rmadataset

An ExpressionSet object with AnnotatedDataFrame metadata.

mfs

A list of 3 ExpressionLevel objects (‘Low’, ‘Medium’ and ‘High’) for each gene (a list of lists).

genes

The set of genes to plot (a vector).

Value

A dataframe with the values of the membership functions (‘Low’, ‘Medium’ and ‘High’) for each gene (in rows) received as a parameter.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

Creates an ExpressionSet with an AnnotatedDataFrame from CSV files

Description

This function creates an ExpressionSet with an AnnotatedDataFrame. To do this, it requires two CSV files in a predefined format:

  1. ‘exprsData’ with the expression values of genes (in rows) of different samples (in columns).

  2. ‘pData’ with the samples (in columns) and the metadata ‘class’ (the most important for the algorithm discriminantFuzzyPattern), ‘age’ and ‘sex’.

Usage

readCSV(fileExprs, filePhenodata)

Arguments

fileExprs

The path to the exprsData file.

filePhenodata

The path to the pData file.

Value

An ExpressionSet object with an AnnotatedDataFrame storing ‘class’, ‘age’ and ‘sex’ information.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

dataDir <- system.file("extdata", package="DFP"); dataDir
fileExprs <- file.path(dataDir, "exprsData.csv"); fileExprs
filePhenodata <- file.path(dataDir, "pData.csv"); filePhenodata
rmadataset <- readCSV(fileExprs, filePhenodata); rmadataset
pData(phenoData(rmadataset))
exprs(rmadataset)[1:10,1:5]

A sample ExpressionSet object

Description

This ExpressionSet object includes an AnnotatedDataFrame with metadata about ‘Disease type’ (the most important for the algorithm), ‘Patient age’ and ‘Patient gender’.
This data set gives the expression values of 500 genes in 35 samples.

Usage

data(rmadataset)

Format

ExpressionSet str(pData(phenoData(rmadataset)))
AnnotatedDataFrame str(exprs(rmadataset))

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

data(rmadataset)
featureNames(rmadataset)[1:20]
sampleNames(rmadataset)
varLabels(rmadataset)
pData(phenoData(rmadataset))
exprs(rmadataset)[1:10,1:5]

Prints the slots (attributes) of an ExpressionLevel object

Description

Prints the slots (center and width) of an "ExpressionLevel" object.

Methods

object = "ExpressionLevel"

See "ExpressionLevel".

Prints the labels to which the algorithm converts the gene expression values

Description

In an intermediate step, the algorithm discriminantFuzzyPattern converts the gene expression values into discrete labels (combining ‘Low’, ‘Medium’ and ‘High’, depending on the value of the param ‘overlapping’).
This function permits printing these labels, specifying a set of genes (a vector) and/or classes of samples.

Usage

showDiscreteValues(dvs, genes, classes)

Arguments

dvs

A matrix with discrete labels for a set of genes (in rows) of several samples (in columns).

genes

[optional] The set of genes to plot.

classes

[optional] A set of classes to which the samples belong. It must be one of the classes stored in the phenoData of the original ExpressionSet object.

Value

A subset of the matrix dvs determined by the restrictions (genes and/or classes).

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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

Plots the Fuzzy Patterns corresponding to a class

Description

This functions prints (in text mode) the Fuzzy Patterns (discrete labels) calculated for a single class of samples.

Usage

showFuzzyPatterns(fps, class)

Arguments

fps

A matrix with the Fuzzy Patterns (discrete labels) for all the samples and genes.

class

A class to which the samples belong. It must be one of the classes stored in the phenoData of the original ExpressionSet rmadataset object.

Value

A vector of Fuzzy Patterns (discrete labels) for a single class of samples, with the genes associated.

Author(s)

Rodrigo Alvarez-Gonzalez
Daniel Glez-Pena
Fernando Diaz
Florentino Fdez-Riverola
Maintainer: Rodrigo Alvarez-Gonzalez <[email protected]>

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