Microbiome research has focused on microorganisms that live within the
human body and their effects on health. During the last few years, the
quantification of microbiome composition in different environments has
been facilitated by the advent of high throughput sequencing
technologies. The statistical challenges include computational
difficulties due to the high volume of data; normalization and
quantification of metabolic abundances, relative taxa and bacterial
genes; high-dimensionality; multivariate analysis; the inherently
compositional nature of the data; and the proper utilization of
complementary phylogenetic information. This has resulted in an
explosion of statistical approaches aimed at tackling the unique
opportunities and challenges presented by microbiome data.
This book provides a comprehensive overview of the state of the art in
statistical and informatics technologies for microbiome research. In
addition to reviewing demonstrably successful cutting-edge methods,
particular emphasis is placed on examples in R that rely on available
statistical packages for microbiome data. With its wide-ranging
approach, the book benefits not only trained statisticians in academia
and industry involved in microbiome research, but also other scientists
working in microbiomics and in related fields.