Statistical methods in molecular biology
著者
書誌事項
Statistical methods in molecular biology
（Methods in molecular biology / John M. Walker, series editor, 620）（Springer protocols）
Humana, c2010
- : hbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Part I: Basic Statistics
1. Experimental Statistics for Biological Sciences
Heejung Bang and Marie Davidian
2. Nonparametric Methods in Molecular Biology
Knut M. Wittkowski and Tingting Song
3. Basics of Bayesian Methods
Sujit K. Ghosh
4. The Bayesian t-Test and Beyond
Mithat Goenen
Part II: Designs and Methods for Molecular Biology
5. Sample Size and Power Calculation for Molecular Biology Studies
Sin-Ho Jung
6. Designs for Linkage Analysis and Association Studies of Complex Diseases
Yuehua Cui, Gengxin Li, Shaoyu Li, and Rongling Wu
7. Introduction to Epigenomics and Epigenome-Wide Analysis
Melissa J. Fazzari and John M. Greally
8. Exploration, Visualization, and Preprocessing of High Dimensional Data
Zhijin Wu and Zhiqiang Wu
Part III: Statistical Methods for Microarray Data
9. Introduction to the Statistical Analysis of Two-Color Microarray Data
Martina Bremer, Edward Himelblau, and Andreas Madlung
10. Building Networks with Microarray Data
Bradley M. Broom, Waree Rinsurongkawong, Lajos Pusztai, and Kim-Anh Do
Part IV: Advanced or Specialized Methods for Molecular Biology
11. Support Vector Machines for Classification: A Statistical Portrait
Yoonkyung Lee
12. An Overview of Clustering Applied to Molecular Biology
Rebecca Nugent and Marina Meila
13. Hidden Markov Model and Its Applications in Motif Findings
Jing Wu and Jun Xie
14. Dimension Reduction for High Dimensional Data
Lexin Li
15. Introduction to the Development and Validation of Predictive Biomarker Models from High-Throughput Datasets
Xutao Deng and Fabien Campagne
16. Multi-GeneExpression-Based Statistical Approaches to Predicting Patients' Clinical Outcomes and Responses
Feng Cheng, Sang-Hoon Cho, and Jae K. Lee
17. Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs
Amy Murphy, Scott T. Weiss, and Christoph Lange
18. Statistical Methods for Proteomics
Klaus Jung
Part V: Meta-Analysis for High-Dimensional Data
19. Statistical Methods for Integrating Multiple Types of High-Throughput Data
Yang Xie and Chul Ahn
20. A Bayesian Hierarchical Model for High-Dimensional Meta Analysis
Fei Liu
21. Methods for Combining Multiple Genome-Wide Linkage Studies
Trecia A. Kippola and Stephanie A. Santorico
Part VI: Other Practical Information
22. Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial Policies
Madhu Mazumdar, Samprit Banerjee, and Heather L. Van Epps
23. Stata Companion
Jennifer Sousa Brennan
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