Chapter from : Metagenomics Current Advances and Emerging Concepts

Metagenomics, the application of high-throughput DNA sequencing for surveys of environmental samples, has revolutionized our view on the taxonomic and genetic composition of complex microbial communities. An enormous richness of microbiota keeps unfolding in the context of various fields ranging from biomedicine and food industry to geology. Primary analysis of metagenomic reads allows to infer semi-quantitative data describing the community structure. However, such compositional data possess statistical specific properties that are important to consider during preprocessing, hypothesis testing and interpreting the results of statistical tests. Failure to account for these specifics may lead to essentially wrong conclusions as a result of the survey. Here we present a researcher introduction to the field of metagenomics with the basic properties of microbial compositional data including statistical power and proposed distribution models, perform a review of the publicly available software tools developed specifically for such data and outline the recommendations for the application of the methods. Introduction Microbiota, complex communities consisting of microbial species, appear to inhabit literally any environmental niche in the world. Recent advances in molecular genetic techniques allowed the study of microbiota in a cultivation-independent way, leading to the discovery of enormous diversity. One of the most advanced and widely used techniques is metagenomic sequencing: classification and quantification of metagenomic sequences can be used Odintsova et al. 18 | to assess both taxonomic and functional composition of microbiota. However, when a researcher is interested in proper statistical assessment of hypothesis regarding the differences between the samples from different groups or its dependence from multiple factors, quantification of metagenomic reads is just an intermediate step. Generally, the specific features and problems of analysing metagenomic data are universal whether the focus is on microbiota of a subway station, saline lake or host-associated communities. However, here we will illustrate these concepts on the example of human microbiota, as it possesses particular interest to researchers due to its essential role in a biomedical context. The human body is a habitat for a great amount of microbes (approximately 1–3% of body mass). They play important roles in biological processes that maintain its vital activity. The microbial community consists of thousands of species, and its structure noticeably varies both among subjects and body parts (Levy and Borenstein, 2013; Stein et al., 2013; Tyakht et al., 2013; Human Microbiome Project Consortium, 2012). As cultivation and examination in vitro of the majority of microorganisms is difficult, metagenomic analysis is one of the most common ways to study the human-associated microbial community structure and functionality. An important part of metagenomic analysis is testing hypotheses about the associations between microbiome structure and some factors. Age, diet type, presence of some disease and so on may play the role of such factors in the case of human microbiome research. Factors may take on discrete or continuous values. Such characteristics as sex, body site and stage of the disease represent discrete ones. They allow us to distinguish two or more groups to compare structure of their microbiota. While designing the groups and the inclusion/ exclusion criteria for a metagenomic survey, a researcher should make sure that the groups are matched and differ only by the selected features. For example, in a case when the microbiota of healthy subjects and patients with dysbiosis are compared, then there should be no substantial differences in weight, age, sex and other parameters between the groups. Age, body mass index and drug dosage are the examples of continuous factors. It is impossible to distinguish groups in such case. So the researcher’s aim is to obtain the functional dependence of microbiome structure on factor value. A study may aim to explore the association of the microbiome with more than one factor at a time. Multifactor analysis is useful when several characteristics may contribute to the changes in microbial structure and each of them is presumed to have substantial influence. Such analysis attempts to estimate the individual impact of each factor. A common metagenomic study of association between clinical data and microbiome composition consists of the following stages (Goodrich et al., 2014): 1 formulation of the aims and experiment design (stating the hypothesis, describing the groups of subjects, determining their minimal size, choosing the optimal sequencing technology, targeted sequencing depth and methods of experimental data analysis); 2 collecting the required number of microbial samples; 3 metagenomic sequencing of each sample; 4 taxonomic profiling for each metagenome; 5 statistical analysis of the compositional data. This review describes basic concepts and models of statistical inference of associations between microbiome structure and factors of interest, with the main focus on human Statistical Analysis of Metagenomic Data | 19 microbiota. It is intended to serve as an assistance in choosing the correct statistical methods for metagenomic analysis realized in R (R Core Team, 2015) and applying them properly (stages 1 and 5). To state the mathematical formulation of the problem it is necessary to get familiar with the data format inherent for metagenomics. We will first briefly describe the first steps of a metagenomic study from collecting samples to obtaining its taxonomic profile. Next, the statistical formulation of the aim of the research and design of an experiment will be discussed. Then we will focus on the main steps and specifics of metagenomic statistical analysis. The last section contains an overview of the actual and widespread approaches to such analysis implemented in R packages. Preparing data for statistical analysis Collection of microbiome samples, sample preparation and sequencing, as well as the primary bioinformatical analysis for taxonomic profiling are complex processes, with each step having its own subtleties. A comprehensive description of them is out of scope of this text and is described elsewhere (for example, in Goodrich et al., 2014). Here we will outline only the essential points. After collecting the microbiota samples from the individuals under investigation, the samples are stored and transported under low temperature and other conditions to prevent changes in the structure of microbial community before the sample gets to a laboratory. There the DNA is extracted from the samples through a multistage process with the use of special reagents. Then a so-called sequencing library is prepared for each sample. The obtained libraries are subject to DNA sequencing resulting in thousands to tens of millions of short nucleotide sequences (reads) corresponding to the genomes of all microorganisms and viruses present in a sample. There are two main types of sequencing: shotgun sequencing (random sequences for the totality of the genetic material are obtained) or amplicon sequencing (reads belong to a fixed gene of each species, most commonly 16S rRNA gene). The resulting reads are subject to quality filtering. Then taxonomical classification is performed so each read is put into correspondence with the available taxonomically annotated database of microbial genomes or genes. It is also possible to perform a de novo analysis without the reference. The result of the taxonomical classification for all reads of a sample is a vector of feature abundances. Each element of this vector reveals the number of reads related to some feature – taxon, gene or gene group. In the context of 16S rRNA sequencing, the common type of feature are referred to as operational taxonomic units (OTUs); as the concept of OTU is not normally used in ‘shotgun’ sequencing, we will use a microbial species as a feature in the text. ‘Shotgun’ sequencing allows us to describe the functional structure of microbial community in addition to its taxonomic structure. It shows semiquantitative portrait of genes, gene groups or metabolic paths in the community. Currently ‘shotgun’ sequencing is a much more costly procedure than the amplicon format. So it is less widespread, albeit it does not give an idea about gene structure of a microbiota. In this paper, without loss of generality, we will describe methods of statistical analysis on the example of 16S rRNA data. The result of the taxonomical classification for all reads of a sample is a vector of feature relative abundance values. Each element of this vector reveals a read count related to some feature (microbial taxon). The microbial communities are compared via the analysis of these vectors. Odintsova et al. 20 | Statistical properties of metagenomic data