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.
General
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