Analysis Graphics

[Inell Krolick]

Theanalysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data.

Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research.

Copyright: 2013 McMurdie, Holmes. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by grant NIH-R01GM086884. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The phyloseq package provides an object-oriented programming infrastructure that simplifies many of the common data management and preprocessing tasks required during analysis of phylogenetic sequencing data. This simplified syntax helps mitigate inconsistency errors and encourages interaction with the data during preprocessing. The phyloseq package also provides a set of powerful analysis and graphics functions, building upon related packages available in R and Bioconductor. It includes or supports some of the most commonly-needed ecology and phylogenetic tools, including a consistent interface for calculating ecological distances and performing dimensional reduction (ordination). The graphics functions allow users to interactively produce annotated publication-quality graphics in just one or two lines of code. The phyloseq package includes extensive documentation in the form of function- and package-level manuals embedded in the package's documentation interface and in a PDF version on Bioconductor [38], as well as extended reproducible examples on the phyloseq homepage [39], and open collaborative development on GitHub [40].

R packages can include example data that is documented with the same help system as other package objects [58]. This data becomes available in the R session by invoking the data function after the package has been loaded. Unless otherwise noted, the examples provided in this manuscript use example data that is included in the phyloseq package.

The workflow starts with the results of OTU clustering and independently-measured sample data (Input, top left), and ends at various analytic procedures available in R for inference and validation. In between are key functions for preprocessing and graphics. Rounded rectangles and diamond shapes represent functions and data objects, respectively, further described in Figure 3.

The phyloseq class is an experiment-level data storage class defined by the phyloseq package for representing phylogenetic sequencing data. Most functions in the phyloseq package expect an instance of this class as their primary argument. See the phyloseq manual [38] for a complete list of functions.

Complementing the data infrastructure, the phyloseq package provides a set of functions that take a phyloseq object as the primary data, and performs an analysis and/or graphics task. Figure 2 summarizes the general workflow within phyloseq, and lists some of the main functions/tools.

There are many combinations of approaches possible (even extending into time-series of table pairs), and the optimal approach depends on the goals of the experiment and characteristics of the data [56]. The phyloseq package also includes a specialized function for displaying ordination results in different ways, described in the following section.

It is important to note that the new phyloseq-class is a significant departure from the originally-proposed phyloseq-class structure [31], which used nested multiple inheritance and a naming convention. It was a valid approach in principle, but was an overly complex approach for the goal of representing a phylogenetic sequencing experiment as a single object. The updated phyloseq-class is simple to extend for developers and easy to explain to users (Figure 3). In general, the downstream analysis and plotting functions that might operate on an instance of the phyloseq-class do not need to (re)perform common validity checks because these checks are consolidated as part of the phyloseq-constructor method.

Analysis tools available in R but not explicitly wrapped in phyloseq are nevertheless available to users and developers via accessors and other data infrastructure tools. This leverages the fact that phyloseq data components are based on standard R data classes and easily used in other package settings in R. For example, we have included example code that illustrates the use of the bioenv function from the vegan package, starting with data represented by the phyloseq-class (See File S2 for code, and the phyloseq demo [86]). Similarly, as an open-source package in an open language/framework (R), phyloseq can be easily included at the relevant steps in pipelines, workbenches, and GUIs now under active development (E.g. ClovR [15], MG-RAST [19], QIIME [11], mcaGUI [88]). This represents a means for investigators with limited programming literacy to still benefit from some of the tools included in, or facilitated by, phyloseq.

Designed and wrote the software described: PJM. Conceived and designed the experiments: PJM SH. Performed the experiments: PJM SH. Analyzed the data: PJM SH. Contributed reagents/materials/analysis tools: PJM SH. Wrote the paper: PJM SH.

The site is secure.

The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Background: the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data.

Results: Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research.

This book starts with elementary properties of the eigenvalues on finite graphs, continues with their estimates and applications, and concludes with heat kernel estimates on infinite graphs and their application to the type problem.

The book is suitable for beginners in the subject and accessible to undergraduate and graduate students with a background in linear algebra I and analysis I. It is based on a lecture course taught by the author and includes a wide variety of exercises. The book will help the reader to reach a level of understanding sufficient to start pursuing research in this exciting area.

Anybody who has ever read a mathematical text of the author would agree that his way of presenting complex material is nothing short of marvelous. This new book showcases again the author's unique ability of presenting challenging topics in a clear and accessible manner, and of guiding the reader with ease to a deep understanding of the subject.

3a8082e126