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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Thisbookisintendedformolecularbiologistswhoperformquantitativeanalysesondata emanatingfromtheir?eldandforthestatisticianswhoworkwithmolecularbiologists andotherbiomedicalresearchers. Therearemanyexcellenttextbooksthatprovidefun- mentalcomponentsforstatisticaltrainingcurricula. Therearealsomany byexpertsfor experts booksinstatisticsandmolecularbiologywhichrequirein-depthknowledgein bothsubjectstobetakenfulladvantageof. Sofar,nobookinstatisticshasbeenpublished thatprovidesthebasicprinciplesofproperstatisticalanalysesandprogressestoamore advancedstatisticsinresponsetorapidlydevelopingtechnologiesandmethodologiesin the?eldofmolecularbiology. Respondingtothissituation,ourbookaimsatbridgingthegapbetweenthesetwo extremes. Molecularbiologistswillbene?tfromtheprogressivestyleofthebookwhere basicstatisticalmethodsareintroducedandgraduallyelevatedtoanintermediatelevel. Similarly,statisticianswillbene?tfromlearningthevariousbiologicaldatageneratedfrom the?eldofmolecularbiology,thetypesofquestionsofinteresttomolecularbiologists, andthestatisticalapproachestoanalyzingthedata. Thestatisticalconceptsandmethods relevanttostudiesinmolecularbiologyarepresentedinasimpleandpracticalmanner. Speci?cally,thebookcoversbasicandintermediatestatisticsthatareusefulforclassical and molecular biology settings and advanced statistical techniques that can be used to helpsolveproblemscommonlyencounteredinmodernmolecularbiologystudies,such assupervisedandunsupervisedlearning,hiddenMarkovmodels,manipulationandan- ysisofdatafromhigh-throughputmicroarrayandproteomicplatform,andsynthesisof these evidences. A tutorial-type format is used to maximize learning in some chapters. Advicefromjournaleditorsonpeer-reviewedpublicationandsomeusefulinformationon softwareimplementationarealsoprovided. Thisbookisrecommendedforuseassupplementarymaterialbothinsideandoutside classroomsorasaself-learningguideforstudents,scientists,andresearcherswhodealwith numericdatainmolecularbiologyandrelated?elds. Thosewhostartasbeginners,but desiretobeatanintermediatelevel,will?ndthisbookespeciallyusefulintheirlearning pathway. WewanttothankJohnWalker(serieseditor),PatrickMarton,DavidCasey,andAnne Meagher,(editorsatSpringerandHumana)andShanthyJaganathan(Integra-India). The followingpersonsprovidedusefuladviceandcommentsonselectionoftopics,referralto expertsineachtopic,and/orchapterreviewsthatwetrulyappreciate:StephenLooney(a former editor of this book), Stan Young, Dmitri Zaykin, Douglas Hawkins, Wei Pan, Alexandre Almeida, John Ho, Rebecca Doerge, Paula Trushin, Kevin Morgan, Jason Osborne,PeterWestfall,JennyXiang,Ya-linChiu,YolandaBarron,HuiboShao,Alvin Mushlin,andRonaldFanta. Drs. Bang,Zhou,andMazumdarwerepartiallysupported byClinicalTranslationalScienceCenter(CTSC)grant(UL1-RR024996). HeejungBang vii Contents Preface…vii Contributors…xi PARTIBASICSTATISTICS…1 1. ExperimentalStatisticsforBiologicalSciences…3 HeejungBangandMarieDavidian 2. NonparametricMethodsforMolecularBiology…105 KnutM. WittkowskiandTingtingSong 3. BasicsofBayesianMethods…155 SujitK. Ghosh 4. TheBayesiant-TestandBeyond …179 MithatGonen PARTII DESIGNSANDMETHODSFORMOLECULARBIOLOGY…201 5. SampleSizeandPowerCalculationforMolecularBiologyStudies…203 Sin-HoJung 6. DesignsforLinkageAnalysisandAssociationStudiesofComplexDiseases…219 YuehuaCui,GengxinLi,ShaoyuLi,andRonglingWu 7. IntroductiontoEpigenomicsandEpigenome-WideAnalysis…243 MelissaJ. FazzariandJohnM. Greally 8. Exploration,Visualization,andPreprocessingofHigh-DimensionalData…267 ZhijinWuandZhiqiangWu PARTIII STATISTICALMETHODSFORMICROARRAYDATA …285 9. IntroductiontotheStatisticalAnalysisofTwo-ColorMicroarrayData…287 MartinaBremer,EdwardHimelblau,andAndreasMadlung 10. BuildingNetworkswithMicroarrayData…315 BradleyM. Broom,WareeRinsurongkawong,LajosPusztai, andKim-AnhDo PARTIV ADVANCEDORSPECIALIZEDMETHODSFORMOLECULARBIOLOGY. . 345 11. SupportVectorMachinesforClassi?cation:AStatisticalPortrait…347 YoonkyungLee 12. AnOverviewofClusteringAppliedtoMolecularBiology …369 RebeccaNugentandMarinaMeila ix xContents 13. HiddenMarkovModelandItsApplicationsinMotifFindings…405 JingWuandJunXie 14. DimensionReductionforHigh-DimensionalData…417 LexinLi 15. IntroductiontotheDevelopmentandValidationofPredictiveBiomarker ModelsfromHigh-ThroughputDataSets …435 XutaoDengandFabienCampagne 16. Multi-geneExpression-basedStatisticalApproachestoPredicting Patients'ClinicalOutcomesandResponses…471 FengCheng,Sang-HoonCho,andJaeK. Lee 17. Two-StageTestingStrategiesforGenome-WideAssociationStudies inFamily-BasedDesigns …485 AmyMurphy,ScottT. Weiss,andChristophLange 18. StatisticalMethodsforProteomics …497 KlausJung PARTVMETA-ANALYSISFORHIGH-DIMENSIONALDATA …509 19. StatisticalMethodsforIntegratingMultipleTypesofHigh-ThroughputData. . 511 YangXieandChulAhn 20. ABayesianHierarchicalModelforHigh-DimensionalMeta-analysis…531 FeiLiu 21. MethodsforCombiningMultipleGenome-WideLinkageStudies…541 TreciaA. KippolaandStephanieA. Santorico PARTVI OTHERPRACTICALINFORMATION …561 22. ImprovedReportingofStatisticalDesignandAnalysis:Guidelines, Education,andEditorialPolicies…5 63 MadhuMazumdar,SampritBanerjee,andHeatherL. VanEpps 23. StataCompanion…599 JenniferSousaBrennan SubjectIndex…627 Contributors CHULAHN* Division of Biostatistics, Department of Clinical Sciences, The Harold C.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Thisbookisintendedformolecularbiologistswhoperformquantitativeanalysesondata emanatingfromtheir?eldandforthestatisticianswhoworkwithmolecularbiologists andotherbiomedicalresearchers. Therearemanyexcellenttextbooksthatprovidefun- mentalcomponentsforstatisticaltrainingcurricula. Therearealsomany byexpertsfor experts booksinstatisticsandmolecularbiologywhichrequirein-depthknowledgein bothsubjectstobetakenfulladvantageof. Sofar,nobookinstatisticshasbeenpublished thatprovidesthebasicprinciplesofproperstatisticalanalysesandprogressestoamore advancedstatisticsinresponsetorapidlydevelopingtechnologiesandmethodologiesin the?eldofmolecularbiology. Respondingtothissituation,ourbookaimsatbridgingthegapbetweenthesetwo extremes. Molecularbiologistswillbene?tfromtheprogressivestyleofthebookwhere basicstatisticalmethodsareintroducedandgraduallyelevatedtoanintermediatelevel. Similarly,statisticianswillbene?tfromlearningthevariousbiologicaldatageneratedfrom the?eldofmolecularbiology,thetypesofquestionsofinteresttomolecularbiologists, andthestatisticalapproachestoanalyzingthedata. Thestatisticalconceptsandmethods relevanttostudiesinmolecularbiologyarepresentedinasimpleandpracticalmanner. Speci?cally,thebookcoversbasicandintermediatestatisticsthatareusefulforclassical and molecular biology settings and advanced statistical techniques that can be used to helpsolveproblemscommonlyencounteredinmodernmolecularbiologystudies,such assupervisedandunsupervisedlearning,hiddenMarkovmodels,manipulationandan- ysisofdatafromhigh-throughputmicroarrayandproteomicplatform,andsynthesisof these evidences. A tutorial-type format is used to maximize learning in some chapters. Advicefromjournaleditorsonpeer-reviewedpublicationandsomeusefulinformationon softwareimplementationarealsoprovided. Thisbookisrecommendedforuseassupplementarymaterialbothinsideandoutside classroomsorasaself-learningguideforstudents,scientists,andresearcherswhodealwith numericdatainmolecularbiologyandrelated?elds. Thosewhostartasbeginners,but desiretobeatanintermediatelevel,will?ndthisbookespeciallyusefulintheirlearning pathway. WewanttothankJohnWalker(serieseditor),PatrickMarton,DavidCasey,andAnne Meagher,(editorsatSpringerandHumana)andShanthyJaganathan(Integra-India). The followingpersonsprovidedusefuladviceandcommentsonselectionoftopics,referralto expertsineachtopic,and/orchapterreviewsthatwetrulyappreciate:StephenLooney(a former editor of this book), Stan Young, Dmitri Zaykin, Douglas Hawkins, Wei Pan, Alexandre Almeida, John Ho, Rebecca Doerge, Paula Trushin, Kevin Morgan, Jason Osborne,PeterWestfall,JennyXiang,Ya-linChiu,YolandaBarron,HuiboShao,Alvin Mushlin,andRonaldFanta. Drs. Bang,Zhou,andMazumdarwerepartiallysupported byClinicalTranslationalScienceCenter(CTSC)grant(UL1-RR024996). HeejungBang vii Contents Preface…vii Contributors…xi PARTIBASICSTATISTICS…1 1. ExperimentalStatisticsforBiologicalSciences…3 HeejungBangandMarieDavidian 2. NonparametricMethodsforMolecularBiology…105 KnutM. WittkowskiandTingtingSong 3. BasicsofBayesianMethods…155 SujitK. Ghosh 4. TheBayesiant-TestandBeyond …179 MithatGonen PARTII DESIGNSANDMETHODSFORMOLECULARBIOLOGY…201 5. SampleSizeandPowerCalculationforMolecularBiologyStudies…203 Sin-HoJung 6. DesignsforLinkageAnalysisandAssociationStudiesofComplexDiseases…219 YuehuaCui,GengxinLi,ShaoyuLi,andRonglingWu 7. IntroductiontoEpigenomicsandEpigenome-WideAnalysis…243 MelissaJ. FazzariandJohnM. Greally 8. Exploration,Visualization,andPreprocessingofHigh-DimensionalData…267 ZhijinWuandZhiqiangWu PARTIII STATISTICALMETHODSFORMICROARRAYDATA …285 9. IntroductiontotheStatisticalAnalysisofTwo-ColorMicroarrayData…287 MartinaBremer,EdwardHimelblau,andAndreasMadlung 10. BuildingNetworkswithMicroarrayData…315 BradleyM. Broom,WareeRinsurongkawong,LajosPusztai, andKim-AnhDo PARTIV ADVANCEDORSPECIALIZEDMETHODSFORMOLECULARBIOLOGY. . 345 11. SupportVectorMachinesforClassi?cation:AStatisticalPortrait…347 YoonkyungLee 12. AnOverviewofClusteringAppliedtoMolecularBiology …369 RebeccaNugentandMarinaMeila ix xContents 13. HiddenMarkovModelandItsApplicationsinMotifFindings…405 JingWuandJunXie 14. DimensionReductionforHigh-DimensionalData…417 LexinLi 15. IntroductiontotheDevelopmentandValidationofPredictiveBiomarker ModelsfromHigh-ThroughputDataSets …435 XutaoDengandFabienCampagne 16. Multi-geneExpression-basedStatisticalApproachestoPredicting Patients'ClinicalOutcomesandResponses…471 FengCheng,Sang-HoonCho,andJaeK. Lee 17. Two-StageTestingStrategiesforGenome-WideAssociationStudies inFamily-BasedDesigns …485 AmyMurphy,ScottT. Weiss,andChristophLange 18. StatisticalMethodsforProteomics …497 KlausJung PARTVMETA-ANALYSISFORHIGH-DIMENSIONALDATA …509 19. StatisticalMethodsforIntegratingMultipleTypesofHigh-ThroughputData. . 511 YangXieandChulAhn 20. ABayesianHierarchicalModelforHigh-DimensionalMeta-analysis…531 FeiLiu 21. MethodsforCombiningMultipleGenome-WideLinkageStudies…541 TreciaA. KippolaandStephanieA. Santorico PARTVI OTHERPRACTICALINFORMATION …561 22. ImprovedReportingofStatisticalDesignandAnalysis:Guidelines, Education,andEditorialPolicies…5 63 MadhuMazumdar,SampritBanerjee,andHeatherL. VanEpps 23. StataCompanion…599 JenniferSousaBrennan SubjectIndex…627 Contributors CHULAHN* Division of Biostatistics, Department of Clinical Sciences, The Harold C.