leukemiasClassifier: Sample leukemias classifier

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.