DFP-package: DFP Package Overview
In DFP: Gene Selection
Description
Details
Author(s)
References
Examples
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:
Calculates the Membership Functions. These functions are used in the next step to discretize gene expression data.
Discretizes the gene expression data (float values) into ‘Low’, ‘Medium’ or ‘High’ labels.
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’).
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 <rodrigo.djv@uvigo.es>
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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
#########################################
############ 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)
Example output
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Center(Low) Width(Low) Center(Medium) Width(Medium) Center(High)
AFFX-BioB-5_at 6.50 0.59 7.10 0.55 7.60
AFFX-BioB-M_at 7.65 0.71 8.36 0.65 8.95
Width(High)
AFFX-BioB-5_at 0.50
AFFX-BioB-M_at 0.59
0C12_S 0C179_S 0C167_S 0C0936_S BP185_S BP355_S BP7644_S
AFFX-BioB-5_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioB-M_at "Low" "Low" "Medium" "High" "Low" "Medium" "High"
AFFX-BioB-3_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioC-5_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioC-3_at "Low" "Low" "Medium" "Medium" "Low" "Low" "High"
AFFX-BioDn-5_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioDn-3_at "Low" "Low" "Medium" "Medium" "Low" "Low" "High"
AFFX-CreX-5_at "Low" "Low" "High" "High" "Low" "Low" "Medium"
AFFX-CreX-3_at "Low" "Low" "High" "High" "Low" "Low" "High"
AFFX-DapX-5_at "Medium" "Low" "Medium" "High" "Low" "Medium" "Medium"
200021_at 200022_at 200023_s_at 200024_at 200025_s_at 200029_at
"High" "Low" "Low" "Low" "Low" "Low"
200031_s_at 200032_s_at 200036_s_at 200042_at 200043_at 200051_at
"Low" "Low" "Low" "Low" "Low" "Low"
200052_s_at 200054_at 200057_s_at 200060_s_at 200061_s_at 200062_s_at
"Low" "Low" "Low" "Low" "Low" "Low"
200063_s_at 200064_at 200077_s_at 200079_s_at 200080_s_at 200081_s_at
"Low" "Low" "High" "Low" "High" "Low"
200088_x_at 200089_s_at 200091_s_at 200092_s_at 200093_s_at 200094_s_at
"Low" "Low" "Low" "Low" "Low" "Low"
healthy APL AML-inv AML-mono AML-other
200002_at "Low" NA "Medium" NA NA
200005_at "Low" NA "High" NA NA
200026_at NA NA "Medium" "Low" NA
200029_at "Low" NA "High" NA NA
200048_s_at NA NA "Low" "Medium" NA
200051_at "Low" NA "High" NA NA
200077_s_at "High" NA "Low" "High" NA
200078_s_at NA NA "Medium" "High" NA
200089_s_at "Low" NA "High" NA NA
200092_s_at "Low" NA "Medium" NA NA
34210_at NA "Low" "High" NA NA
35160_at "High" NA "Medium" NA NA
35820_at "Low" NA NA "High" NA
36994_at "High" NA "Medium" NA NA
37590_g_at NA NA "Medium" "Low" NA
37966_at "Medium" "Low" "High" NA NA
39705_at "High" "Low" NA NA NA
40016_g_at "Low" NA "High" "Low" NA
40420_at "High" NA "Low" NA NA
40829_at "High" NA "Medium" NA NA
attr(,"ifs")
healthy APL AML-inv AML-mono AML-other
200002_at 1.00 0.5714286 1.0000000 0.6 0.5625
200005_at 1.00 0.5714286 1.0000000 0.6 0.4375
200026_at 0.75 0.4285714 1.0000000 1.0 0.6250
200029_at 1.00 0.4285714 1.0000000 0.6 0.4375
200048_s_at 0.50 0.8571429 1.0000000 1.0 0.3125
200051_at 1.00 0.4285714 1.0000000 0.4 0.5625
200077_s_at 1.00 0.5714286 1.0000000 1.0 0.5625
200078_s_at 0.75 0.5714286 1.0000000 1.0 0.5000
200089_s_at 1.00 0.5714286 1.0000000 0.6 0.5000
200092_s_at 1.00 0.2857143 1.0000000 0.4 0.6875
34210_at 0.75 1.0000000 1.0000000 0.6 0.3750
35160_at 1.00 0.5714286 1.0000000 0.6 0.4375
35820_at 1.00 0.7142857 0.6666667 1.0 0.3750
36994_at 1.00 0.5714286 1.0000000 0.6 0.4375
37590_g_at 0.75 0.8571429 1.0000000 1.0 0.3750
37966_at 1.00 1.0000000 1.0000000 0.6 0.4375
39705_at 1.00 1.0000000 0.6666667 0.6 0.5625
40016_g_at 1.00 0.5714286 1.0000000 1.0 0.5000
40420_at 1.00 0.5714286 1.0000000 0.6 0.4375
40829_at 1.00 0.5714286 1.0000000 0.6 0.3750
DFP documentation built on Nov. 8, 2020, 7:46 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References Examples
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:
Calculates the Membership Functions. These functions are used in the next step to discretize gene expression data.
Discretizes the gene expression data (float values) into ‘Low’, ‘Medium’ or ‘High’ labels.
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’).
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 <rodrigo.djv@uvigo.es>
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | #########################################
############ 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)
|
Example output
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Center(Low) Width(Low) Center(Medium) Width(Medium) Center(High)
AFFX-BioB-5_at 6.50 0.59 7.10 0.55 7.60
AFFX-BioB-M_at 7.65 0.71 8.36 0.65 8.95
Width(High)
AFFX-BioB-5_at 0.50
AFFX-BioB-M_at 0.59
0C12_S 0C179_S 0C167_S 0C0936_S BP185_S BP355_S BP7644_S
AFFX-BioB-5_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioB-M_at "Low" "Low" "Medium" "High" "Low" "Medium" "High"
AFFX-BioB-3_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioC-5_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioC-3_at "Low" "Low" "Medium" "Medium" "Low" "Low" "High"
AFFX-BioDn-5_at "Low" "Low" "Medium" "Medium" "Low" "Medium" "High"
AFFX-BioDn-3_at "Low" "Low" "Medium" "Medium" "Low" "Low" "High"
AFFX-CreX-5_at "Low" "Low" "High" "High" "Low" "Low" "Medium"
AFFX-CreX-3_at "Low" "Low" "High" "High" "Low" "Low" "High"
AFFX-DapX-5_at "Medium" "Low" "Medium" "High" "Low" "Medium" "Medium"
200021_at 200022_at 200023_s_at 200024_at 200025_s_at 200029_at
"High" "Low" "Low" "Low" "Low" "Low"
200031_s_at 200032_s_at 200036_s_at 200042_at 200043_at 200051_at
"Low" "Low" "Low" "Low" "Low" "Low"
200052_s_at 200054_at 200057_s_at 200060_s_at 200061_s_at 200062_s_at
"Low" "Low" "Low" "Low" "Low" "Low"
200063_s_at 200064_at 200077_s_at 200079_s_at 200080_s_at 200081_s_at
"Low" "Low" "High" "Low" "High" "Low"
200088_x_at 200089_s_at 200091_s_at 200092_s_at 200093_s_at 200094_s_at
"Low" "Low" "Low" "Low" "Low" "Low"
healthy APL AML-inv AML-mono AML-other
200002_at "Low" NA "Medium" NA NA
200005_at "Low" NA "High" NA NA
200026_at NA NA "Medium" "Low" NA
200029_at "Low" NA "High" NA NA
200048_s_at NA NA "Low" "Medium" NA
200051_at "Low" NA "High" NA NA
200077_s_at "High" NA "Low" "High" NA
200078_s_at NA NA "Medium" "High" NA
200089_s_at "Low" NA "High" NA NA
200092_s_at "Low" NA "Medium" NA NA
34210_at NA "Low" "High" NA NA
35160_at "High" NA "Medium" NA NA
35820_at "Low" NA NA "High" NA
36994_at "High" NA "Medium" NA NA
37590_g_at NA NA "Medium" "Low" NA
37966_at "Medium" "Low" "High" NA NA
39705_at "High" "Low" NA NA NA
40016_g_at "Low" NA "High" "Low" NA
40420_at "High" NA "Low" NA NA
40829_at "High" NA "Medium" NA NA
attr(,"ifs")
healthy APL AML-inv AML-mono AML-other
200002_at 1.00 0.5714286 1.0000000 0.6 0.5625
200005_at 1.00 0.5714286 1.0000000 0.6 0.4375
200026_at 0.75 0.4285714 1.0000000 1.0 0.6250
200029_at 1.00 0.4285714 1.0000000 0.6 0.4375
200048_s_at 0.50 0.8571429 1.0000000 1.0 0.3125
200051_at 1.00 0.4285714 1.0000000 0.4 0.5625
200077_s_at 1.00 0.5714286 1.0000000 1.0 0.5625
200078_s_at 0.75 0.5714286 1.0000000 1.0 0.5000
200089_s_at 1.00 0.5714286 1.0000000 0.6 0.5000
200092_s_at 1.00 0.2857143 1.0000000 0.4 0.6875
34210_at 0.75 1.0000000 1.0000000 0.6 0.3750
35160_at 1.00 0.5714286 1.0000000 0.6 0.4375
35820_at 1.00 0.7142857 0.6666667 1.0 0.3750
36994_at 1.00 0.5714286 1.0000000 0.6 0.4375
37590_g_at 0.75 0.8571429 1.0000000 1.0 0.3750
37966_at 1.00 1.0000000 1.0000000 0.6 0.4375
39705_at 1.00 1.0000000 0.6666667 0.6 0.5625
40016_g_at 1.00 0.5714286 1.0000000 1.0 0.5000
40420_at 1.00 0.5714286 1.0000000 0.6 0.4375
40829_at 1.00 0.5714286 1.0000000 0.6 0.3750
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