Table Of ContentICSA Book Series in Statistics
Series Editors:
Jiahua Chen · Ding-Geng Chen
Yinglin Xia · Jun Sun · Ding-Geng Chen
Statistical
Analysis of
Microbiome
Data with R
ICSA Book Series in Statistics
Series editors
JiahuaChen,DepartmentofStatistics,UniversityofBritishColumbia,Vancouver,
Canada
Ding-Geng Chen, Department of Biostatistics, School of Social Work,
University of North Carolina, Chapel Hill, NC, USA
The ICSA Book Series in Statistics showcases research from the International
ChineseStatisticalAssociationthathasaninternationalreach.Itpublishesbooksin
statistical theory, applications, and statistical education. All books are associated
withtheICSAorareauthoredbyinvitedcontributors.Booksmaybemonographs,
edited volumes, textbooks and proceedings.
More information about this series at http://www.springer.com/series/13402
Yinglin Xia Jun Sun Ding-Geng Chen
(cid:129) (cid:129)
Statistical Analysis
of Microbiome Data with R
123
Yinglin Xia Ding-GengChen
Department ofMedicine Schoolof Social Work
University of Illinois at Chicago University of NorthCarolina
Chicago, IL, USA ChapelHill, NC,USA
Jun Sun and
Department ofMedicine
University of Illinois at Chicago Department ofBiostatistics
Chicago, IL, USA Gillings Schoolof Global
PublicHealth
University of NorthCarolina
ChapelHill, NC,USA
and
Department ofStatistics
University of Pretoria
Pretoria, SouthAfrica
ISSN 2199-0980 ISSN 2199-0999 (electronic)
ICSABookSeries inStatistics
ISBN978-981-13-1533-6 ISBN978-981-13-1534-3 (eBook)
https://doi.org/10.1007/978-981-13-1534-3
LibraryofCongressControlNumber:2018950827
©SpringerNatureSingaporePteLtd.2018
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Yinglin Xia and Jun Sun would like to
dedicate this book to their parents and
parents-in-law: Qijia Xia, Xincui Wang,
Zong-Xiang Sun, and Xiao-Yun Fu; and their
sons: Yuxuan Xia and Jason Xia for their
constant love and support.
Ding-Geng Chen would like to dedicate this
book to his parents and parents-in-law who
value high-education and hard-working, and
tohiswife,Ke,hisson,JohnD.Chen,andhis
daughter, Jenny K. Chen, for their love and
support.
Preface
Microbiome research and microbiome data analysis are one of the fast-growing
areas in biomedical and public health research. It is evidenced and catalyzed by
publicationsindifferentrelevantfieldsofstudiesandmethodologydevelopment,as
well as large-scale projects, such as the Human Microbiome Project (HMP), the
IntegrativeHumanMicrobiomeProject,andMetagenomicsoftheHumanIntestinal
Tract (MetaHIT) study. By the end of 2017, HMP investigators have already
publishedover650scientificpapers,citedover70,000times(https://commonfund.
nih.gov/hmp).
We are now advancing our understanding of how the microbiome impacts
humanhealthanddisease,withmoreandmoreprojectsinmicrobiomefundedand
research and statistical methodology papers published. The masses of microbiome
datageneratedby16SrRNAsequencingandshotgunmetagenomicsequencingvia
the bioinformatics pipelines (packages), promote recent major growth spurt of
microbiomestudy.Dataanalysisandmethodologyareintegralpartsofmicrobiome
research. Since microbiome data are very complicated, there is a critical need to
develop all kinds of statistical methodologies for microbiome research, ranging
from application to methodology, and to statistical theory.
The habit of human learning starts with the known, then processes to the
unknown. Statistical analysis of microbiome data follows with the similar process.
Inthebeginning,theresearchersandstatisticiansusedtheclassicstatisticalmethods
and models or borrowed them from other relevant fields, such as ecology and
microarray. Later, they developed their own statistical methods and models that
target one or more unique features of microbiome data. Currently, statistical
methodsandanalysistoolsforanalyzingmicrobiomedataareavailablefromclassic
statistics, relevant research fields, and new developments, including visualization
and characterization of structure of microbiome data sets.
Statistical tools for performing microbiome data analysis are now available in
different languages and environments across different platforms, either in
web-based or programming-based approaches. Obviously, R system and environ-
ment play a critical role in developing statistical tools for analyzing microbiome
data.
vii
viii Preface
The birth of this book is an excellent example to show how a multidisciplinary
teamworkingtogethertomeettheneedofthefield.InApril2016,Dr.JunSunwas
workingonamicrobiomebookonbehalfoftheAmericanPhysiologicalSocietyby
Springer. She invited Dr. Yinglin Xia to contribute a book chapter on microbiome
data analysis. Dr. Sun and Dr. Xia have long-time collaborations in biomedical
sciences including microbiome studies. While working on the book chapter, they
thoughtitwouldbeagoodideatoexpandabriefbookchaptertoacomprehensive
bookonanalyzingmicrobiomedata.TheywereveryhappythatDr.Ding-GengChen
was willing to join the team and provide his expertise on statistics and microarray
study.InMay2016,abookproposalonStatisticalAnalysisofMicrobiomeDatawith
Rwassubmitted.Itwaswellreceivedbythepeer-reviewandfullysupportedbythe
editorsofICSABookSeriesinStatistics.
In this book, we aim to provide the step-by-step procedures to perform data
analysis of microbiome data by way of the R programming language. We provide
somebioinformaticandstatisticalfoundationsofdataanalysisbecausemicrobiome
data are complicated and analysis of microbiome data is still very challenging. To
strike a balance, we briefly introduce concepts, backgrounds, statistical method
developments before illustrating the applications in real data.
Thebookwasorganizedinthisway:inthebeginningthreechapters,wespecially
providedoverviewandintroductionofbioinformatics,featuresofmicrobiomedata,
andstatisticalanalysisofmicrobiomedata.InChap.4,wecoveredsomebasicskills
in R programming, RStudio, ggplot2, and most often used R packages and tech-
niques for microbiome data management and programming. In Chap. 5, we intro-
ducedclassicandnewlydevelopedmethodsinapplicationofhypothesistestingand
power analysis of microbiome data. Chapter 6 focused on introduction of commu-
nityalphaandbetameasuresandcalculations.Chapter7providedmostoftenused
visualization techniques for exploratory analysis of microbiome data including
graphic summary of data and clustering, ordination. Chapters 8 and 9 focused on
univariate and multivariate community analysis, respectively. Many classic and
newlydevelopedmethods areintroducedintheapplicationofmicrobiome studies.
We contributed Chap. 10 to compositional analysis of microbiome data. In this
chapter, we introduced basic concept, fundamental principles, brief history, proce-
dures, and challenges of compositional data analysis. We also summarized several
considerationsofmicrobiomedatasetbeingtreatedascompositionalandillustrated
compositional analysis of microbiome data using real data. Chapters 11 and 12
focused on count-based approaches of modeling over-dispersed and zero-inflated
microbiome data, respectively. Here, we widely covered statistical methods and
models of count data, including negative binomial, zero-inflated, and zero-hurdle
models,andzero-inflatedBetaregressionmodelwithrandom-effectsinlongitudinal
setting.Wealsodiscussedtheconceptadjustmentofmodelapplicationandtopicsof
model comparisons.
We hope the contents of these chapters and the way of organization provide a
framework of statistical analysis of microbiome data. We expect this book to be
used by (1) statisticians, who are working on microbiome studies, either for their
ownresearch,orfortheircollaborativeresearch,suchasexperimentaldesign,grant
Preface ix
application, and data analysis; (2) researchers from microbiome and biomedical
fields, such as principal investigators, clinicians, research fellows, graduate stu-
dents,whoaredesigningthestudies,collectingthedata;(3)researchersfromother
relevantorsimilarfields(e.g.,bioinformatics,ecology,microarray,economics,etc.)
and common use of statistical methods and R packages. The data and R codes
used in this book are available by requesting to the first author: Yinglin Xia at
[email protected].
Chicago, IL, USA Yinglin Xia
Chicago, IL, USA Jun Sun
North Carolina, NC, USA Ding-Geng Chen
Pretoria, South Africa
June, 2018
Acknowledgements
There are a number of people we wish to thank. We greatly appreciate those
persons, who helped us from various resources and at different stages of devel-
opmentandwriting.ThanksgototheeditorialteamatSpringerfortheirenthusiasm
in supporting this project and for their feedback along the way of process to
publishing. Broadly, we greatly appreciate the developers of statistical methods,
models, and R packages and R system and environment in general. Some of them
sharedtheirpaperswithus.Withouttheirgreatworks,thebookcannotbeavailable
in current breadth and scope. We especially wish thanks to Jason for his support
and proofreading of some chapters. Finally, we all wish to express our deepest
appreciation to our respective families for their love, patience, and support during
the writing of this book.
xi
