Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data

312 Pages·2012·8.27 MB·English

by  M. Dehmer

Editedby
FrankEmmert-Streiband
MatthiasDehmer
StatisticalDiagnostics
forCancer
TitlesoftheSeries
“QuantitativeandNetworkBiology”
Volume1
Dehmer,M.,Emmert-Streib,F.,Graber,A.,Salvador,A.(eds.)
AppliedStatisticsforNetworkBiology
MethodsinSystemsBiology
2011
ISBN:978-3-527-32750-8
Volume2
Dehmer,M.,Varmuza,K.,Bonchev,D.(eds.)
StatisticalModellingofMolecular
DescriptorsinQSAR/QSPR
2012
ISBN:978-3-527-32434-7
RelatedTitles
Zhou,X.-H.,Obuchowski,N.A.,McClish,D.K.
StatisticalMethodsinDiagnosticMedicine
2011
ISBN:978-0-470-18314-4
Azuaje,F.
Bioinformatics andBiomarkerDiscovery
“Omic”DataAnalysisforPersonalizedMedicine
2010
ISBN:978-0-470-74460-4
Quantitative and Network Biology
Series Editors M. Dehmer and F. Emmert-Streib
Volume 3
Statistical Diagnostics for Cancer
AnalyzingHigh-DimensionalData
Editedby
FrankEmmert-StreibandMatthiasDehmer
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V
Contents
Preface XIII
ListofContributors XVII
PartOne GeneralOverview 1
1 ControlofTypeIErrorRatesforOncologyBiomarkerDiscovery
withHigh-ThroughputPlatforms 3
JeffreyMiecznikowski,DanWang,andSongLiu
1.1 BriefSummary 3
1.2 Introduction 3
1.3 High-ThroughputPlatforms 4
1.3.1 GeneExpressionArrays 5
1.3.2 RNA-Seq 5
1.3.3 DNAMethylationArrays 6
1.3.4 MassSpectrometryPlatforms 6
1.3.5 aCGHArrays 7
1.3.6 PreprocessingHTPlatforms 7
1.4 AnalysisofExperiments 8
1.4.1 LinearRegression 8
1.4.1.1 SimpleLinearRegression 9
1.4.1.2 MultipleRegression 11
1.4.2 LogisticRegression(YDiscrete) 11
1.4.2.1 MultipleLogisticRegression 13
1.4.3 SurvivalModeling 13
1.4.3.1 Kaplan–MeierAnalysis 13
1.5 MultipleTestingTypeIErrors 15
1.5.1 FWER,k-FWERMethods 17
1.5.1.1 AdjustedBonferroniMethod 17
1.5.1.2 HolmProcedure 17
1.5.1.3 GeneralizedHochbergProcedure 18
1.5.1.4 GeneralizedS9id(cid:1)akProcedure 18
1.5.1.5 minPandmaxTprocedures 19
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VI Contents
1.6 Discussion 19
1.7 Perspective 20
References 21
2 OverviewofPublicCancerDatabases,Resources,
andVisualizationTools 27
FrankEmmert-Streib,RicardodeMatosSimoes,ShaileshTripathi,
andMatthiasDehmer
2.1 BriefOverview 27
2.2 Introduction 27
2.3 DifferentCancerTypesareGeneticallyRelated 28
2.4 IncidenceandMortalityRatesofCancer 29
2.5 CancerandDisorderDatabases 30
2.6 VisualizationandNetwork-BasedAnalysisTools 34
2.6.1 Web-BasedSoftware 34
2.6.2 R-BasedPackages 34
2.7 Conclusions 35
2.8 Perspective 37
References 37
PartTwo BayesianMethods 41
3 DiscoveryofExpressionSignaturesinChronicMyeloidLeukemia
byBayesianModelAveraging 43
KaYeeYeung
3.1 BriefIntroduction 43
3.2 ChronicMyeloidLeukemia(CML) 44
3.3 VariableSelectiononGeneExpressionData 44
3.4 BayesianModelAveraging(BMA) 46
3.4.1 TheIterativeBMAAlgorithm(iBMA) 47
3.4.2 ComputationalAssessment 48
3.5 CaseStudy:CMLProgressionData 49
3.6 ThePowerofiBMA 50
3.7 LaboratoryValidation 51
3.8 Conclusions 52
3.9 Perspective 53
3.10 PubliclyAvailableResources 54
References 54
4 BayesianRankingandSelectionMethodsinMicroarrayStudies 57
HisashiNomaandShigeyukiMatsui
4.1 BriefSummary 57
4.2 Introduction 57
4.3 HierarchicalMixtureModelingandEmpiricalBayesEstimation 59
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Contents VII
4.4 RankingandSelectionMethods 60
4.4.1 RankingBasedonEffectSizes 60
4.4.1.1 PosteriorMean(PM) 61
4.4.1.2 RankPosteriorMean(RPM) 61
4.4.1.3 Tail-AreaPosteriorProbability(TPP) 62
4.4.2 RankingBasedonSelectionAccuracyofDifferentialGenes 63
4.4.2.1 PosteriorProbabilityofDifferentiallyExpressed(PPDE) 63
4.4.2.2 EvaluatingSelectionAccuracy 64
4.5 Simulations 65
4.6 Application 67
4.7 ConcludingRemarks 71
4.8 Perspective 72
4.9 Appendix:TheEMAlgorithm 72
References 73
5 MulticlassClassificationviaBayesianVariableSelection
withGeneExpressionData 75
YangAijun,SongXinyuan,andLiYunxian
5.1 BriefSummary 75
5.2 Introduction 75
5.3 MatrixVariateDistribution 77
5.4 Method 77
5.4.1 Model 77
5.4.2 PriorSpecification 79
5.4.3 Computation 80
5.4.4 Classification 82
5.5 RealDataAnalysis 83
5.5.1 LeukemiaData 83
5.5.2 LymphomaData 87
5.5.3 ComputationalTime 89
5.6 Discussion 89
5.7 Perspective 89
References 90
6 SemisupervisedMethodsforAnalyzingHigh-dimensional
GenomicData 93
DevinC.Koestler
6.1 BriefSummary 93
6.2 Motivation 93
6.3 ExistingApproaches 95
6.3.1 FullyUnsupervisedProcedures 96
6.3.2 FullySupervisedProcedures 96
6.3.3 SemisupervisedProcedures 97
6.3.3.1 SemisupervisedClustering 99
6.3.3.2 SemisupervisedRPMM 100
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VIII Contents
6.3.3.3 ConsiderationsRegardingSemisupervisedProcedures 101
6.4 DataApplication:MesotheliomaCancerDataSet 102
6.4.1 Results:MesotheliomaCancerDataSet 104
6.5 Perspective 105
References 106
PartThree Network-BasedApproaches 107
7 ColorectalCancerandItsMolecularSubsystems:Construction,
Interpretation,andValidation 109
VishalN.PatelandMarkR.Chance
7.1 BriefSummary 109
7.2 ColonCancer:Etiology 109
7.3 ColonCancer:Development 110
7.4 ThePathwayParadigm 111
7.5 CancerSubtypesandTherapies 112
7.6 MolecularSubsystems:Introduction 113
7.7 MolecularSubsystems:Construction 113
7.7.1 Measurements 113
7.7.2 Manifolds 114
7.8 MolecularSubsystems:Interpretation 117
7.8.1 Examples 117
7.9 MolecularSubsystems:Validation 119
7.10 WorkedExample:Label-FreeProteomics 120
7.10.1 WholeProtein-LevelSignificance 122
7.10.2 Peptide-LevelSignificance 122
7.10.3 Exon-LevelSignificance 125
7.10.4 SummarizingtheResults 126
7.11 Conclusions 127
7.12 Perspective 128
References 129
8 NetworkMedicine:DiseaseGenesinMolecularNetworks 133
SreenivasChavaliandKartiekKanduri
8.1 BriefSummary 133
8.2 Introduction 133
8.3 GeneticArchitectureofHumanDiseases 134
8.4 SystemsPropertiesofDiseaseGenes 136
8.4.1 NetworkMeasures 136
8.4.2 DiseaseandDisease-GeneNetworks 137
8.4.3 DiseaseGenesinProteinInteractionNetworks 139
8.4.4 IdentificationofDiseaseModules 143
8.5 DiseaseGenePrioritization 145
8.5.1 LinkageMethods 145
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Contents IX
8.5.2 Disease-Module-BasedMethods 146
8.5.3 Diffusion-BasedMethods 147
8.6 Conclusion 147
8.7 Perspectives 148
References 148
9 InferenceofGeneRegulatoryNetworksinBreastandOvarianCancer
byIntegratingDifferentGenomicData 153
BinhuaTang,FeiGu,andVictorX.Jin
9.1 BriefSummary 153
9.2 Introduction 153
9.3 TheoryandContentsofGeneRegulatoryNetwork 154
9.3.1 BasicTheoryofGeneRegulatoryNetwork 154
9.3.2 ContentofGeneRegulatoryNetwork 155
9.3.2.1 IdentifyandInfertheStructurePropertiesandRegulatory
RelationshipsofGeneNetworks 155
9.3.2.2 UnderstandtheBasicRulesofGeneExpressionandFunction 155
9.3.2.3 DiscovertheTransferRulesofGeneticInformationDuringGene
Expression 155
9.3.2.4 StudyontheGeneFunctioninaSystematicFramework 156
9.4 InferenceofGeneRegulatoryNetworksinHumanCancer 156
9.4.1 TheInSilicoAnalyticalApproach 156
9.4.1.1 StudyCase1:InferenceofStaticGeneRegulatoryNetworkof
Estrogen-DependentBreastCancerCellLine 158
9.4.1.2 StudyCase2:GeneRegulatoryNetworkofGenome-WideMappingof
TGFb/SMAD4TargetsinOvarianCancerPatients 160
9.4.2 ABayesianInferenceApproachforGeneticRegulatoryAnalysis 164
9.4.2.1 StudyCase:ERaTranscriptionalRegulatoryDynamicsin
BreastCancerCell 165
9.5 Conclusions 167
9.6 Perspective 168
References 169
10 Network-Module-BasedApproachesinCancerDataAnalysis 173
GuanmingWuandLincolnStein
10.1 BriefSummary 173
10.2 Introduction 173
10.3 NotationandTerminology 174
10.4 NetworkModulesContainingFunctionallySimilarGenesor
Proteins 174
10.5 NetworkModuleSearchingMethods 175
10.5.1 GreedyNetworkModuleSearchAlgorithms 175
10.5.2 ObjectiveFunctionGuidedSearch 176
10.5.3 NetworkClusteringAlgorithms 176
10.5.4 CommunitySearchAlgorithms 177
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X Contents
10.5.5 MutualExclusivity-BasedSearchAlgorithms 178
10.5.6 WeightedGeneExpressionNetwork 178
10.6 ApplicationsofNetwork-Module-BasedApproaches
inCancerStudies 179
10.6.1 NetworkModulesandCancerPrognosticSignatures 179
10.6.2 CancerDriverGeneSearchBasedonNetworkModules 179
10.6.3 UsingNetworkPatternstoIdentifyCancerMechanisms 180
10.7 TheReactomeFICytoscapePlug-in 180
10.7.1 ConstructionofaFunctionalInteractionNetwork 181
10.7.2 NetworkClusteringAlgorithm 181
10.7.3 CancerGeneIndexDataSet 181
10.7.4 AnalyzingtheTCGAOVMutationDataSet 182
10.7.4.1 LoadingtheMutationFileintoCytoscapeandConstructingaFI
Subnetwork 182
10.7.4.2 NetworkClusteringandNetworkModuleFunctionalAnalysis 184
10.7.4.3 Module-BasedSurvivalAnalysis 186
10.7.4.4 CancerGeneIndexDataOverlayAnalysis 187
10.8 Conclusions 189
10.9 Perspective 189
References 191
11 DiscriminantandNetworkAnalysistoStudyOriginofCancer 193
LiChen,YeTian,GuoqiangYu,DavidJ.Miller,Ie-MingShih,andYueWang
11.1 BriefSummary 193
11.2 Introduction 193
11.3 OverviewofRelevantMachineLearningTechniques 194
11.3.1 Fisher’sDiscriminantAnalysisandANOVA 194
11.3.2 HierarchicalClustering 195
11.3.3 One-Versus-AllSupportVectorMachineandNearest-Mean
Classifier 196
11.3.4 DifferentialDependencyNetwork 197
11.4 Methods 198
11.4.1 CNADataAnalysisforTestingExistenceofMonoclonality 198
11.4.1.1 Preprocessing 200
11.4.1.2 AssessingStatisticalSignificanceofMonoclonality 200
11.4.1.3 VisualizationofMonoclonality 201
11.4.2 ATwo-StageAnalyticalMethodforTestingtheOriginofCancer 201
11.4.2.1 BasicAssumptions 202
11.4.2.2 TissueHeterogeneityCorrection 203
11.4.2.3 Stage1:FeatureSelectionandClassification 203
11.4.2.4 Stage2:TranscriptionalNetworkComparison 204
11.5 ExperimentsandResults 204
11.5.1 Monoclonality 204
11.5.1.1 TestingExistenceofMonoclonality 204
11.5.1.2 TheSignificanceofMonoclonality 206