leukemiasClassifier: Sample leukemias classifier
In geNetClassifier: Classify diseases and build associated gene networks using gene expression profiles
Description
Usage
Format
Examples
Description
A sample of the object returned by geNetClassifier. Containins the classifier, the network, and the gene statistics.
Usage
1
Format
GeNetClassifierReturn object
Examples
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data(leukemiasClassifier)
# Global view of the object and its structure:
leukemiasClassifier
names(leukemiasClassifier)
# Call and acess to the classifier:
leukemiasClassifier@call
leukemiasClassifier@classifier
# Genes used for training the classifier:
numGenes(leukemiasClassifier@classificationGenes)
leukemiasClassifier@classificationGenes
genesDetails(leukemiasClassifier@classificationGenes)
# Generalization Error estimated by cross-validation:
# leukemiasClassifier@generalizationError
# overview(leukemiasClassifier@generalizationError)
# List of Networks by classes:
leukemiasClassifier@genesNetwork
# Access to the nodes or edges of each network:
getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,]
getNodes(leukemiasClassifier@genesNetwork$AML)[1:50]
# Global genes ranking:
leukemiasClassifier@genesRanking
numGenes(leukemiasClassifier@genesRanking)
numSignificantGenes(leukemiasClassifier@genesRanking)
# getTopRanking(leukemiasClassifier@genesRanking, 10)
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, cbind, colMeans, colSums, colnames, 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")'.
Loading required package: EBarrays
Loading required package: lattice
Loading required package: minet
R object summary:
Classifier trained with 50 samples.
Total number of genes included in the classifier: 26.
Number of genes per class:
ALL AML CLL CML NoL
9 5 1 5 6
For classificationGenes details: genesDetails(EXAMPLE@classificationGenes)
Generalization error and gene stats calculated through 5-fold cross-validation:
[1] "accuracy" "sensitivitySpecificity"
[3] "confMatrix" "probMatrix"
[5] "querySummary" "classificationGenes.stats"
[7] "classificationGenes.num"
The ranking of all genes contains (genes per class):
ALL AML CLL CML NoL
2342 3023 2824 2539 3049
The networks calculated for the topGenes genes of each class contain:
ALL AML CLL CML NoL
Number of genes 1027 400 1916 949 400
Number of relations 1942 296 18506 6540 1993
Available slots in this R object:
[1] "call" "classifier" "classificationGenes"
[4] "generalizationError" "genesRanking" "genesRankingType"
[7] "genesNetwork" "genesNetworkType"
To see an overview of all available slots type "overview(EXAMPLE)"
[1] "call" "classifier" "classificationGenes"
[4] "generalizationError" "genesRanking" "genesRankingType"
[7] "genesNetwork" "genesNetworkType"
geNetClassifier(eset = leukEset_protCoding[, trainSamples], sampleLabels = "LeukemiaType",
plotsName = "leukemiasClassifier", buildClassifier = TRUE,
estimateGError = TRUE, calculateNetwork = TRUE, geneLabels = geneSymbols)
$SVMclassifier
Call:
svm.default(x = t(esetFilteredDataFrame[buildGenesVector, trainSamples]),
y = sampleLabels[trainSamples], kernel = "linear", probability = T,
C = 1)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 1
gamma: 0.03846154
Number of Support Vectors: 29
ALL AML CLL CML NoL
9 5 1 5 6
Top ranked genes for the classes: ALL AML CLL CML NoL
ALL AML CLL CML NoL
[1,] "VPREB1" "HOXA9" "TYMS" "GJB6" "FGF13"
[2,] "ZNF423" "MEIS1" NA "PRG3" "NMU"
[3,] "DNTT" "CD24L4" NA "LY86" "SMPDL3A"
[4,] "EBF1" "ANGPT1" NA "ABP1" "KLRB1"
[5,] "PXDN" "CCNA1" NA "TRIM22" "RNF182"
[6,] "S100A16" NA NA NA "RFESD"
[7,] "CSRP2" NA NA NA NA
[8,] "SOCS2" NA NA NA NA
[9,] "CTGF" NA NA NA NA
Details of the top X ranked genes of each class: genesDetails(..., nGenes=X)
$ALL
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000169575 VPREB1 1 1.0 ALL 1 6.3307
ENSG00000102935 ZNF423 2 3.0 ALL 1 5.0980
ENSG00000107447 DNTT 3 2.8 ALL 1 6.8948
ENSG00000164330 EBF1 4 3.8 ALL 1 5.4171
ENSG00000130508 PXDN 5 5.2 ALL 1 5.0387
ENSG00000188643 S100A16 6 5.4 ALL 1 4.3434
ENSG00000175183 CSRP2 7 7.8 ALL 1 4.0479
ENSG00000120833 SOCS2 8 10.8 ALL 1 4.5383
ENSG00000118523 CTGF 9 14.8 ALL 1 3.6167
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000169575 UP 9.416945 ALL FALSE
ENSG00000102935 UP 13.240579 ALL TRUE
ENSG00000107447 UP 8.978735 ALL TRUE
ENSG00000164330 UP 10.515557 ALL TRUE
ENSG00000130508 UP 8.657167 ALL TRUE
ENSG00000188643 UP 12.385161 ALL TRUE
ENSG00000175183 UP 8.782649 ALL TRUE
ENSG00000120833 UP 8.697958 ALL FALSE
ENSG00000118523 UP 5.551344 ALL FALSE
$AML
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000078399 HOXA9 1 1.2 AML 1 4.4362
ENSG00000143995 MEIS1 2 3.0 AML 1 3.2785
ENSG00000185275 CD24L4 3 3.8 AML 1 -4.4926
ENSG00000154188 ANGPT1 4 4.8 AML 1 2.7427
ENSG00000133101 CCNA1 5 5.4 AML 1 2.5558
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000078399 UP 8.011524 AML FALSE
ENSG00000143995 UP 10.318618 AML TRUE
ENSG00000185275 DOWN -5.734254 AML FALSE
ENSG00000154188 UP 9.219579 AML FALSE
ENSG00000133101 UP 8.249562 AML FALSE
$CLL
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000176890 TYMS 1 NA CLL 1 -5.5184
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000176890 DOWN -10.07534 CLL FALSE
$CML
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000121742 GJB6 1 2.2 CML 1 5.2528
ENSG00000156575 PRG3 2 92.4 CML 1 4.9751
ENSG00000112799 LY86 3 39.6 CML 1 -2.2047
ENSG00000002726 ABP1 4 5.0 CML 1 2.5110
ENSG00000132274 TRIM22 5 35.8 CML 1 -2.6736
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000121742 UP 4.943174 CML FALSE
ENSG00000156575 UP 4.090488 CML TRUE
ENSG00000112799 DOWN -5.560448 CML FALSE
ENSG00000002726 UP 8.477016 CML FALSE
ENSG00000132274 DOWN -9.054268 CML FALSE
$NoL
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000129682 FGF13 1 1.2 NoL 1 2.6907
ENSG00000109255 NMU 2 9.0 NoL 1 1.9662
ENSG00000172594 SMPDL3A 3 13.8 NoL 1 1.9532
ENSG00000111796 KLRB1 4 22.2 NoL 1 2.2347
ENSG00000180537 RNF182 5 5.6 NoL 1 1.8442
ENSG00000175449 RFESD 6 5.8 NoL 1 2.3698
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000129682 UP 3.788266 NoL FALSE
ENSG00000109255 UP 4.100963 NoL FALSE
ENSG00000172594 UP 5.072272 NoL FALSE
ENSG00000111796 UP 3.395340 NoL FALSE
ENSG00000180537 UP 1.063461 NoL FALSE
ENSG00000175449 UP 2.946852 NoL FALSE
$ALL
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 1027
Number of edges (relationships): [1] 1942
$AML
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 400
Number of edges (relationships): [1] 296
$CLL
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 1916
Number of edges (relationships): [1] 18506
$CML
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 949
Number of edges (relationships): [1] 6540
$NoL
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 400
Number of edges (relationships): [1] 1993
gene1 class1 gene2 class2 relation
[1,] "ENSG00000078399" "AML" "ENSG00000143995" "AML" "Correlation - pearson"
[2,] "ENSG00000154188" "AML" "ENSG00000198795" "AML" "Correlation - pearson"
[3,] "ENSG00000078399" "AML" "ENSG00000106004" "AML" "Correlation - pearson"
[4,] "ENSG00000154188" "AML" "ENSG00000155792" "AML" "Correlation - pearson"
[5,] "ENSG00000119919" "AML" "ENSG00000108511" "AML" "Correlation - pearson"
value
[1,] "0.922460476283629"
[2,] "0.804443836092871"
[3,] "0.836149615702043"
[4,] "0.815177435058601"
[5,] "0.940367679337551"
[1] "ENSG00000078399" "ENSG00000143995" "ENSG00000185275" "ENSG00000154188"
[5] "ENSG00000133101" "ENSG00000198795" "ENSG00000106004" "ENSG00000155792"
[9] "ENSG00000119919" "ENSG00000106236" "ENSG00000148154" "ENSG00000108511"
[13] "ENSG00000012779" "ENSG00000177508" "ENSG00000092529" "ENSG00000111057"
[17] "ENSG00000153807" "ENSG00000165072" "ENSG00000197576" "ENSG00000128805"
[21] "ENSG00000122592" "ENSG00000105991" "ENSG00000185559" "ENSG00000087245"
[25] "ENSG00000151491" "ENSG00000003436" "ENSG00000152580" "ENSG00000167236"
[29] "ENSG00000233101" "ENSG00000134138" "ENSG00000121690" "ENSG00000163106"
[33] "ENSG00000145777" "ENSG00000164120" "ENSG00000147465" "ENSG00000180044"
[37] "ENSG00000052126" "ENSG00000115183" "ENSG00000113396" "ENSG00000171502"
[41] "ENSG00000179241" "ENSG00000003096" "ENSG00000120093" "ENSG00000180767"
[45] "ENSG00000183691" "ENSG00000020181" "ENSG00000087495" "ENSG00000179542"
[49] "ENSG00000157303" "ENSG00000147650"
Top ranked genes for the classes: ALL AML CLL CML NoL
ALL AML CLL CML NoL
[1,] "VPREB1" "HOXA9" "TYMS" "GJB6" "FGF13"
[2,] "ZNF423" "MEIS1" "FCER2" "PRG3" "NMU"
[3,] "DNTT" "CD24L4" "NUCB2" "LY86" "SMPDL3A"
[4,] "EBF1" "ANGPT1" "RRAS2" "ABP1" "KLRB1"
[5,] "PXDN" "CCNA1" "PNOC" "TRIM22" "RNF182"
[6,] "S100A16" "ZNF521" "C6orf105" "NLRC3" "RFESD"
[7,] "CSRP2" "HOXA5" "RRM2" "LPXN" "SLC25A21"
[8,] "SOCS2" "DEPDC6" "KIAA0101" "GBP3" "CD160"
[9,] "CTGF" "NKX2-3" "UHRF1" "TNS3" "CLIC2"
[10,] "COL5A1" "NPTX2" "ABCA6" "ZC3H12D" "TMEM56"
...
Number of ranked significant genes (posterior probability over 0.95 threshold):
ALL AML CLL CML NoL
799 213 1579 658 154
To see the whole ranking (3049 rows) use: getRanking(...)
Details of the top X ranked genes of each class: genesDetails(..., nGenes=X)
ALL AML CLL CML NoL
2342 3023 2824 2539 3049
ALL AML CLL CML NoL
799 213 1579 658 154
geNetClassifier documentation built on Nov. 8, 2020, 4:53 p.m.
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Description Usage Format Examples
Description
A sample of the object returned by geNetClassifier. Containins the classifier, the network, and the gene statistics.
Usage
1 |
Format
GeNetClassifierReturn object
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | data(leukemiasClassifier)
# Global view of the object and its structure:
leukemiasClassifier
names(leukemiasClassifier)
# Call and acess to the classifier:
leukemiasClassifier@call
leukemiasClassifier@classifier
# Genes used for training the classifier:
numGenes(leukemiasClassifier@classificationGenes)
leukemiasClassifier@classificationGenes
genesDetails(leukemiasClassifier@classificationGenes)
# Generalization Error estimated by cross-validation:
# leukemiasClassifier@generalizationError
# overview(leukemiasClassifier@generalizationError)
# List of Networks by classes:
leukemiasClassifier@genesNetwork
# Access to the nodes or edges of each network:
getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,]
getNodes(leukemiasClassifier@genesNetwork$AML)[1:50]
# Global genes ranking:
leukemiasClassifier@genesRanking
numGenes(leukemiasClassifier@genesRanking)
numSignificantGenes(leukemiasClassifier@genesRanking)
# getTopRanking(leukemiasClassifier@genesRanking, 10)
|
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, cbind, colMeans, colSums, colnames, 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")'.
Loading required package: EBarrays
Loading required package: lattice
Loading required package: minet
R object summary:
Classifier trained with 50 samples.
Total number of genes included in the classifier: 26.
Number of genes per class:
ALL AML CLL CML NoL
9 5 1 5 6
For classificationGenes details: genesDetails(EXAMPLE@classificationGenes)
Generalization error and gene stats calculated through 5-fold cross-validation:
[1] "accuracy" "sensitivitySpecificity"
[3] "confMatrix" "probMatrix"
[5] "querySummary" "classificationGenes.stats"
[7] "classificationGenes.num"
The ranking of all genes contains (genes per class):
ALL AML CLL CML NoL
2342 3023 2824 2539 3049
The networks calculated for the topGenes genes of each class contain:
ALL AML CLL CML NoL
Number of genes 1027 400 1916 949 400
Number of relations 1942 296 18506 6540 1993
Available slots in this R object:
[1] "call" "classifier" "classificationGenes"
[4] "generalizationError" "genesRanking" "genesRankingType"
[7] "genesNetwork" "genesNetworkType"
To see an overview of all available slots type "overview(EXAMPLE)"
[1] "call" "classifier" "classificationGenes"
[4] "generalizationError" "genesRanking" "genesRankingType"
[7] "genesNetwork" "genesNetworkType"
geNetClassifier(eset = leukEset_protCoding[, trainSamples], sampleLabels = "LeukemiaType",
plotsName = "leukemiasClassifier", buildClassifier = TRUE,
estimateGError = TRUE, calculateNetwork = TRUE, geneLabels = geneSymbols)
$SVMclassifier
Call:
svm.default(x = t(esetFilteredDataFrame[buildGenesVector, trainSamples]),
y = sampleLabels[trainSamples], kernel = "linear", probability = T,
C = 1)
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 1
gamma: 0.03846154
Number of Support Vectors: 29
ALL AML CLL CML NoL
9 5 1 5 6
Top ranked genes for the classes: ALL AML CLL CML NoL
ALL AML CLL CML NoL
[1,] "VPREB1" "HOXA9" "TYMS" "GJB6" "FGF13"
[2,] "ZNF423" "MEIS1" NA "PRG3" "NMU"
[3,] "DNTT" "CD24L4" NA "LY86" "SMPDL3A"
[4,] "EBF1" "ANGPT1" NA "ABP1" "KLRB1"
[5,] "PXDN" "CCNA1" NA "TRIM22" "RNF182"
[6,] "S100A16" NA NA NA "RFESD"
[7,] "CSRP2" NA NA NA NA
[8,] "SOCS2" NA NA NA NA
[9,] "CTGF" NA NA NA NA
Details of the top X ranked genes of each class: genesDetails(..., nGenes=X)
$ALL
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000169575 VPREB1 1 1.0 ALL 1 6.3307
ENSG00000102935 ZNF423 2 3.0 ALL 1 5.0980
ENSG00000107447 DNTT 3 2.8 ALL 1 6.8948
ENSG00000164330 EBF1 4 3.8 ALL 1 5.4171
ENSG00000130508 PXDN 5 5.2 ALL 1 5.0387
ENSG00000188643 S100A16 6 5.4 ALL 1 4.3434
ENSG00000175183 CSRP2 7 7.8 ALL 1 4.0479
ENSG00000120833 SOCS2 8 10.8 ALL 1 4.5383
ENSG00000118523 CTGF 9 14.8 ALL 1 3.6167
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000169575 UP 9.416945 ALL FALSE
ENSG00000102935 UP 13.240579 ALL TRUE
ENSG00000107447 UP 8.978735 ALL TRUE
ENSG00000164330 UP 10.515557 ALL TRUE
ENSG00000130508 UP 8.657167 ALL TRUE
ENSG00000188643 UP 12.385161 ALL TRUE
ENSG00000175183 UP 8.782649 ALL TRUE
ENSG00000120833 UP 8.697958 ALL FALSE
ENSG00000118523 UP 5.551344 ALL FALSE
$AML
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000078399 HOXA9 1 1.2 AML 1 4.4362
ENSG00000143995 MEIS1 2 3.0 AML 1 3.2785
ENSG00000185275 CD24L4 3 3.8 AML 1 -4.4926
ENSG00000154188 ANGPT1 4 4.8 AML 1 2.7427
ENSG00000133101 CCNA1 5 5.4 AML 1 2.5558
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000078399 UP 8.011524 AML FALSE
ENSG00000143995 UP 10.318618 AML TRUE
ENSG00000185275 DOWN -5.734254 AML FALSE
ENSG00000154188 UP 9.219579 AML FALSE
ENSG00000133101 UP 8.249562 AML FALSE
$CLL
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000176890 TYMS 1 NA CLL 1 -5.5184
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000176890 DOWN -10.07534 CLL FALSE
$CML
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000121742 GJB6 1 2.2 CML 1 5.2528
ENSG00000156575 PRG3 2 92.4 CML 1 4.9751
ENSG00000112799 LY86 3 39.6 CML 1 -2.2047
ENSG00000002726 ABP1 4 5.0 CML 1 2.5110
ENSG00000132274 TRIM22 5 35.8 CML 1 -2.6736
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000121742 UP 4.943174 CML FALSE
ENSG00000156575 UP 4.090488 CML TRUE
ENSG00000112799 DOWN -5.560448 CML FALSE
ENSG00000002726 UP 8.477016 CML FALSE
ENSG00000132274 DOWN -9.054268 CML FALSE
$NoL
GeneName ranking gERankMean class postProb exprsMeanDiff
ENSG00000129682 FGF13 1 1.2 NoL 1 2.6907
ENSG00000109255 NMU 2 9.0 NoL 1 1.9662
ENSG00000172594 SMPDL3A 3 13.8 NoL 1 1.9532
ENSG00000111796 KLRB1 4 22.2 NoL 1 2.2347
ENSG00000180537 RNF182 5 5.6 NoL 1 1.8442
ENSG00000175449 RFESD 6 5.8 NoL 1 2.3698
exprsUpDw discriminantPower discrPwClass isRedundant
ENSG00000129682 UP 3.788266 NoL FALSE
ENSG00000109255 UP 4.100963 NoL FALSE
ENSG00000172594 UP 5.072272 NoL FALSE
ENSG00000111796 UP 3.395340 NoL FALSE
ENSG00000180537 UP 1.063461 NoL FALSE
ENSG00000175449 UP 2.946852 NoL FALSE
$ALL
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 1027
Number of edges (relationships): [1] 1942
$AML
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 400
Number of edges (relationships): [1] 296
$CLL
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 1916
Number of edges (relationships): [1] 18506
$CML
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 949
Number of edges (relationships): [1] 6540
$NoL
Attribute summary of the GenesNetwork:
Number of nodes (genes): [1] 400
Number of edges (relationships): [1] 1993
gene1 class1 gene2 class2 relation
[1,] "ENSG00000078399" "AML" "ENSG00000143995" "AML" "Correlation - pearson"
[2,] "ENSG00000154188" "AML" "ENSG00000198795" "AML" "Correlation - pearson"
[3,] "ENSG00000078399" "AML" "ENSG00000106004" "AML" "Correlation - pearson"
[4,] "ENSG00000154188" "AML" "ENSG00000155792" "AML" "Correlation - pearson"
[5,] "ENSG00000119919" "AML" "ENSG00000108511" "AML" "Correlation - pearson"
value
[1,] "0.922460476283629"
[2,] "0.804443836092871"
[3,] "0.836149615702043"
[4,] "0.815177435058601"
[5,] "0.940367679337551"
[1] "ENSG00000078399" "ENSG00000143995" "ENSG00000185275" "ENSG00000154188"
[5] "ENSG00000133101" "ENSG00000198795" "ENSG00000106004" "ENSG00000155792"
[9] "ENSG00000119919" "ENSG00000106236" "ENSG00000148154" "ENSG00000108511"
[13] "ENSG00000012779" "ENSG00000177508" "ENSG00000092529" "ENSG00000111057"
[17] "ENSG00000153807" "ENSG00000165072" "ENSG00000197576" "ENSG00000128805"
[21] "ENSG00000122592" "ENSG00000105991" "ENSG00000185559" "ENSG00000087245"
[25] "ENSG00000151491" "ENSG00000003436" "ENSG00000152580" "ENSG00000167236"
[29] "ENSG00000233101" "ENSG00000134138" "ENSG00000121690" "ENSG00000163106"
[33] "ENSG00000145777" "ENSG00000164120" "ENSG00000147465" "ENSG00000180044"
[37] "ENSG00000052126" "ENSG00000115183" "ENSG00000113396" "ENSG00000171502"
[41] "ENSG00000179241" "ENSG00000003096" "ENSG00000120093" "ENSG00000180767"
[45] "ENSG00000183691" "ENSG00000020181" "ENSG00000087495" "ENSG00000179542"
[49] "ENSG00000157303" "ENSG00000147650"
Top ranked genes for the classes: ALL AML CLL CML NoL
ALL AML CLL CML NoL
[1,] "VPREB1" "HOXA9" "TYMS" "GJB6" "FGF13"
[2,] "ZNF423" "MEIS1" "FCER2" "PRG3" "NMU"
[3,] "DNTT" "CD24L4" "NUCB2" "LY86" "SMPDL3A"
[4,] "EBF1" "ANGPT1" "RRAS2" "ABP1" "KLRB1"
[5,] "PXDN" "CCNA1" "PNOC" "TRIM22" "RNF182"
[6,] "S100A16" "ZNF521" "C6orf105" "NLRC3" "RFESD"
[7,] "CSRP2" "HOXA5" "RRM2" "LPXN" "SLC25A21"
[8,] "SOCS2" "DEPDC6" "KIAA0101" "GBP3" "CD160"
[9,] "CTGF" "NKX2-3" "UHRF1" "TNS3" "CLIC2"
[10,] "COL5A1" "NPTX2" "ABCA6" "ZC3H12D" "TMEM56"
...
Number of ranked significant genes (posterior probability over 0.95 threshold):
ALL AML CLL CML NoL
799 213 1579 658 154
To see the whole ranking (3049 rows) use: getRanking(...)
Details of the top X ranked genes of each class: genesDetails(..., nGenes=X)
ALL AML CLL CML NoL
2342 3023 2824 2539 3049
ALL AML CLL CML NoL
799 213 1579 658 154
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