Statistical Methods in Molecular Biology (eng)

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 MithatGönen 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. Kippolaan

While there is a wide selection of 'by experts, for experts’ books in statistics and molecular biology, there is a distinct need for a book that presents the basic principles of proper statistical analyses and progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology.  Statistical Methods in Molecular Biology strives to fill that gap by covering basic and intermediate statistics that are useful for classical molecular biology settings and advanced statistical techniques that can be used to help solve problems commonly encountered in modern molecular biology studies, such as supervised and unsupervised learning, hidden Markov models, methods for manipulation and analysis of high-throughput microarray and proteomic data, and methods for the synthesis of the available evidences. This detailed volume offers molecular biologists a book in a progressive style where basic statistical methods are introduced and gradually elevated to an intermediate level, while providing statisticians knowledge of various biological data generated from the field of molecular biology, the types of questions of interest to molecular biologists, and the state-of-the-art statistical approaches to analyzing the data.  As a volume in the highly successful Methods in Molecular Biology™ series, this work provides the kind of meticulous descriptions and implementation advice for diverse topics that are crucial for getting optimal results.
 
Comprehensive but convenient, Statistical Methods in Molecular Biology will aid students, scientists, and researchers along the pathway from beginning strategies to a deeper understanding of these vital systems of data analysis and interpretation within one concise volume.

 

"Here is a comprehensive book that systematically covers both basic and advanced statistical topics in molecular biology, including parametric and nonparametric, and frequentist and Bayesian methods.  I am highly impressed by the breadth and depth of the applications. I strongly recommend this book for both statisticians and biologists who need to communicate with each other in this exciting field of research."

- Robert C. Elston, PhD., Director, Division of Genetic and Molecular Epidemiology, Case Western Reserve University

 

"An extraordinary exposition of the central topics of modern molecular biology, presented by practicing experts who weave together rigorous theory with practical techniques and illustrative examples."

- George C. Newman, MD, PhD, Chairman, Neurosensory Sciences, Albert Einstein Medical Center

 

"I cannot think of anything we need now in translation research field more than more efficient cross talk between molecular biology and statistics. This book is just on target. It fills the gap."

- Iman Osman, MB, BCh, MD, Director, Interdisciplinary Melanoma Cooperative Program, New York University Langone Medical Center