rmadataset: A sample ExpressionSet object
In DFP: Gene Selection

Description Usage Format Author(s) References Examples

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.

1	data(rmadataset)

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

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

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

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’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, 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")'.

 [1] "AFFX-BioB-5_at"  "AFFX-BioB-M_at"  "AFFX-BioB-3_at"  "AFFX-BioC-5_at" 
 [5] "AFFX-BioC-3_at"  "AFFX-BioDn-5_at" "AFFX-BioDn-3_at" "AFFX-CreX-5_at" 
 [9] "AFFX-CreX-3_at"  "AFFX-DapX-5_at"  "AFFX-DapX-M_at"  "AFFX-DapX-3_at" 
[13] "AFFX-LysX-5_at"  "AFFX-LysX-M_at"  "AFFX-LysX-3_at"  "AFFX-PheX-5_at" 
[17] "AFFX-PheX-M_at"  "AFFX-PheX-3_at"  "AFFX-ThrX-5_at"  "AFFX-ThrX-M_at" 
 [1] "0C12_S"    "0C179_S"   "0C167_S"   "0C0936_S"  "AP5204_S"  "AP10222_S"
 [7] "AP12366_S" "AP13058_S" "AP13223_S" "AP14217_S" "AP14398_S" "BP185_S"  
[13] "BP355_S"   "BP7644_S"  "CP6667_S"  "CP9949_S"  "CP12361_S" "CP13701_S"
[19] "CP13774_S" "XP139_S"   "XP10557_S" "XP13296_S" "XP13451_S" "XP14399_S"
[25] "XP14698_S" "XP15443_S" "XP170_S"   "XP6209_S"  "XP7297_S"  "XP9376_S" 
[31] "XP9875_S"  "XP10232_S" "XP11567_S" "XP14735_S" "XP16942_S"
[1] "class" "age"   "sex"  
              class age sex
0C12_S      healthy   ?   ?
0C179_S     healthy   ?   ?
0C167_S     healthy   ?   ?
0C0936_S    healthy   ?   ?
AP5204_S        APL  38   F
AP10222_S       APL  40   M
AP12366_S       APL  43   M
AP13058_S       APL  70   M
AP13223_S       APL  14   F
AP14217_S       APL  43   F
AP14398_S       APL  35   F
BP185_S     AML-inv  32   M
BP355_S     AML-inv  47   M
BP7644_S    AML-inv  21   M
CP6667_S   AML-mono  20   F
CP9949_S   AML-mono  39   F
CP12361_S  AML-mono  31   F
CP13701_S  AML-mono  23   F
CP13774_S  AML-mono  63   M
XP139_S   AML-other  31   M
XP10557_S AML-other  36   M
XP13296_S AML-other  49   M
XP13451_S AML-other  47   F
XP14399_S AML-other  66   F
XP14698_S AML-other  26   M
XP15443_S AML-other  44   F
XP170_S   AML-other  26   F
XP6209_S  AML-other  31   F
XP7297_S  AML-other  70   M
XP9376_S  AML-other  50   M
XP9875_S  AML-other  44   F
XP10232_S AML-other  66   F
XP11567_S AML-other  55   M
XP14735_S AML-other  32   M
XP16942_S AML-other  24   M
                   0C12_S   0C179_S   0C167_S  0C0936_S  AP5204_S
AFFX-BioB-5_at   6.294903  6.408961  7.106451  7.314986  6.694213
AFFX-BioB-M_at   7.712930  7.699704  8.557743  8.749660  7.805106
AFFX-BioB-3_at   6.619992  6.885540  7.598040  7.639675  6.827084
AFFX-BioC-5_at   8.085707  7.909092  9.059624  8.819587  8.562031
AFFX-BioC-3_at   7.623914  7.771220  8.438279  8.314519  7.987099
AFFX-BioDn-5_at  7.845967  7.732062  8.679445  8.810465  8.143035
AFFX-BioDn-3_at 11.170271 10.238159 11.700467 11.561583 11.436912
AFFX-CreX-5_at  11.690336 10.985007 12.246246 12.343182 11.915992
AFFX-CreX-3_at  12.265124 12.007440 12.768115 12.789332 12.560627
AFFX-DapX-5_at   3.636848  3.563022  3.600715  3.732374  3.568412