Table Of ContentSpringer Handbooks of Computational Statistics
Henry Horng-Shing Lu
Bernhard Schölkopf
Martin T. Wells
Hongyu Zhao Editors
Handbook
of Statistical
Bioinformatics
Second Edition
Springer Handbooks of Computational Statistics
SeriesEditors
JamesE.Gentle,GeorgeMasonUniversity,Fairfax,VA,USA
WolfgangKarlHärdle,Humboldt-UniversitätzuBerlin,Berlin,Germany
YuichiMori,OkayamaUniversityofScience,Okayama,Japan
Henry Horng-Shing Lu (cid:129) Bernhard Schölkopf (cid:129)
Martin T. Wells (cid:129) Hongyu Zhao
Editors
Handbook of Statistical
Bioinformatics
Editors
HenryHorng-ShingLu BernhardSchölkopf
InstituteofStatistics DepartmentofEmpiricalInference
NationalYangMingChiaoTungUniversity MaxPlanckInstituteforIntelligentSystems
Hsinchu,Taiwan,ROC Tübingen,Germany
MartinT.Wells HongyuZhao
DepartmentofStatisticsandDataScience DepartmentofBiostatistics
CornellUniversity YaleUniversity
Ithaca,NY,USA NewHaven,CT,USA
ISSN2197-9790 ISSN2197-9804 (electronic)
SpringerHandbooksofComputationalStatistics
ISBN978-3-662-65901-4 ISBN978-3-662-65902-1 (eBook)
https://doi.org/10.1007/978-3-662-65902-1
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Preface
Numerous fascinating and important breakthroughs in biotechnology have gen-
erated massive volumes of high throughput data with diverse types that demand
novel developments of efficient and appropriate tools in computational statistics
that are integrated with biologicalknowledge and computationalalgorithms. This
updated volume collects contributed chapters from leading researchers to survey
manyrecentactiveresearchtopicsthathavedevelopedsincethepreviouseditionof
the HandbookofStatistical Bioinformatics.This updatedhandbookis intendedto
serveasbothanintroductoryandreferencemonographforstudentsandresearchers
who are interested in learning the state-of-the-art developments in computational
statisticsasappliedtocomputationalbiology.
This collectionof articles, from the leading scholarsin the field, is primarily a
monographwhichwillbeofinteresttotheeducational,academic,andprofessional
organizationsrelatedtostatisticians,computerscientists,biologicalandbiomedical
researcherswithstronginterestsincomputationalbiology.Althoughthereareother
volumes available for computational statistics and bioinformatics on the market,
therearefewbookssuchasthisthatfocusontheinterfacebetweencomputational
statisticsandcutting-edgedevelopmentsincomputationalbiology.Seeingthisneed,
thiscompletelyupdatedcollectionisaimedtoestablishthisbridge.Thishandbook
covers many significant up-to-date topics in probabilistic and statistical modeling
aswellastheanalysisofmassivedatasetsgeneratedfrommodernbiotechnology.
Thesemethodsandtechnologieswillchangetheperspectivesofbiology,healthcare,
and medicine in the twenty-first century! This collection is an extended version
of the previous edited handbook. The advanced research topics cover statistical
methodsforsingle-cellanalysis,networkanalysis,andsystemsbiology.
During the editing process of this handbook, the world has been upended by
the massive influence of COVID-19 pandemic and other challenges. The editors
wouldliketothankthecontributingauthors,Springermanagementteammembers,
v
vi Preface
supportingcolleaguesandfamilymembersfortheirincrediblesupportandpatience
duringthischallengingtimeperiodinorderforthishandbooktobemadeavailable
totherelatedscholarlycommunities!
Hsinchu,Taiwan,ROC HenryHorng-ShingLu
Tübingen,Germany BernhardSchölkopf
Ithaca,NY,USA MartinT.Wells
NewHaven,CT,USA HongyuZhao
May8,2022
Contents
PartI Single-CellAnalysis
ComputationalandStatisticalMethodsforSingle-CellRNA
SequencingData.................................................................. 3
ZuohengWangandXitingYan
Pre-processing, Dimension Reduction, and Clustering for
Single-CellRNA-seqData....................................................... 37
JialuHu,YiranWang,XiangZhou,andMengjieChen
IntegrativeAnalysesofSingle-CellMulti-OmicsData:AReview
fromaStatisticalPerspective................................................... 53
ZhixiangLin
ApproachestoMarker Gene IdentificationfromSingle-Cell
RNA-SequencingData........................................................... 71
RonnieY.Li,WenjingMa,andZhaohuiS.Qin
Model-BasedClusteringofSingle-CellOmicsData.......................... 85
XinjunWang,HaoranHu,andWeiChen
DeepLearningMethodsforSingle-CellOmicsData......................... 109
JingshuWangandTianyuChen
PartII NetworkAnalysis
ProbabilisticGraphicalModelsforGeneRegulatoryNetworks ........... 135
ZhenweiZhou,XiaoyuZhang,PeitaoWu,andChing-TiLiu
Additive ConditionalIndependence forLargeandComplex
BiologicalStructures............................................................. 153
Kuang-YaoLee,BingLi,andHongyuZhao
IntegrationofBooleanandBayesianNetworks .............................. 173
Meng-YuanTsaiandHenryHorng-ShingLu
vii
viii Contents
ComputationalMethods for Identifying MicroRNA-Gene
RegulatoryModules ............................................................. 187
YinLiu
CausalInferenceinBiostatistics................................................ 209
ShashaHanandXiao-HuaZhou
BayesianBalanceMediationAnalysisinMicrobiomeStudies.............. 237
LuHuangandHongzheLi
PartIII SystemsBiology
IdentifyingGeneticLociAssociatedwithComplexTraitVariability...... 257
JiachengMiaoandQiongshiLu
CellType-SpecificAnalysisforHigh-throughputData...................... 271
ZiyiLiandHaoWu
RecentDevelopmentofComputationalMethodsinthe Field
ofEpitranscriptomics............................................................ 285
ZijieZhang,ShunLiu,ChuanHe,andMengjieChen
EstimationofTumorImmuneSignaturesfromTranscriptomicsData.... 311
XiaoqingYu
Cross-LinkingMassSpectrometryDataAnalysis............................ 339
ChenZhouandWeichuanYu
Cis-regulatoryElementFrequencyModulesandtheirPhase
TransitionacrossHominidae ................................................... 371
LeiMLi,MengtianLi,andLiangLi
ImprovedMethodforRootingandTip-DatingaViralPhylogeny......... 397
XuhuaXia
Part I
Single-Cell Analysis
Computational and Statistical Methods
for Single-Cell RNA Sequencing Data
ZuohengWang andXitingYan
Abstract In recent years, advances in droplet-based technology have boosted
the popularity of using single-cell RNA sequencing (scRNA-seq) technology to
investigate transcriptomic and cell population composition changes in various
tissues and diseases. Despite the potential of these technologies in understanding
disease pathogenesis and developing novel personalized therapeutics, analyses
of the generated scRNA-seq data are challenging, mainly due to high noise
level, prevalent dropout events, heterogeneous sources of variation confounding
phenotypeof interest, and so on. In this chapter,we introducethese challengesin
analyses of scRNA-seq data and the corresponding computational and statistical
methods developed to address them. The topics include data preprocessing, data
normalization,dropoutimputation,anddifferentialexpressionanalysis.
1 Introduction
Gene expressionprofilingmeasureslevels of mRNA to understandtranscriptomic
changes due to disease, treatment, environment,time, and so on. Traditional bulk
RNAgeneexpressionprofilingusingmicroarraysandRNAsequencingpoolsRNAs
fromalargepopulationofcellsconsistingofvariousandoftenunknowncelltypes.
Itmeasurestheaverageexpressionprofileinmixedcellpopulationswithunknown
contribution from different cells or cell types. Thus, bulk RNA gene expression
dataisunabletopreciselyidentifythecellularsourceoftranscriptomicchangesof
interest,especiallywhenhighcell-to-cellheterogeneityexists[1–5].Toinvestigate
transcriptomicchangesatsingle-cellresolution,twomajorchallengesexistinclud-
ing (1) isolating cells from each other without strong perturbations to cells that
lead to systematic transcriptomic changes and (2) amplification of extremely low
Z.Wang·X.Yan((cid:2))
YaleUniversity,NewHaven,CT,USA
e-mail:[email protected];[email protected]
©TheAuthor(s),underexclusivelicensetoSpringer-VerlagGmbH,DE, 3
partofSpringerNature2022
H.H.-S.Luetal.(eds.),HandbookofStatisticalBioinformatics,
SpringerHandbooksofComputationalStatistics,
https://doi.org/10.1007/978-3-662-65902-1_1
