vegan: Community Ecology Package
01 Jan 2012-
About: The article was published on 2012-01-01 and is currently open access. It has received 15937 citations till now.
Citations
More filters
TL;DR: The phyloseq project for R is a new open-source software package dedicated to the object-oriented representation and analysis of microbiome census data in R, which supports importing data from a variety of common formats, as well as many analysis techniques.
Abstract: 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. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
11,272 citations
TL;DR: The landscape of gene expression across tissues is described, thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants are cataloged, complex network relationships are described, and signals from genome-wide association studies explained by eQTLs are identified.
Abstract: Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysi...
4,418 citations
TL;DR: It is advocated that investigators avoid rarefying altogether and supported statistical theory is provided that simultaneously accounts for library size differences and biological variability using an appropriate mixture model.
Abstract: Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.
2,184 citations
TL;DR: The R package poppr is developed providing unique tools for analysis of data from admixed, clonal, mixed, and/or sexual populations, and functions for genotypic diversity and clone censoring are specific for clonal populations.
Abstract: Many microbial, fungal, or oomcyete populations violate assumptions for population genetic analysis because these populations are clonal, admixed, partially clonal, and/or sexual. Furthermore, few tools exist that are specifically designed for analyzing data from clonal populations, making analysis difficult and haphazard. We developed the R package poppr providing unique tools for analysis of data from admixed, clonal, mixed, and/or sexual populations. Currently, poppr can be used for dominant/codominant and haploid/diploid genetic data. Data can be imported from several formats including GenAlEx formatted text files and can be analyzed on a user-defined hierarchy that includes unlimited levels of subpopulation structure and clone censoring. New functions include calculation of Bruvo’s distance for microsatellites, batch-analysis of the index of association with several indices of genotypic diversity, and graphing including dendrograms with bootstrap support and minimum spanning networks. While functions for genotypic diversity and clone censoring are specific for clonal populations, several functions found in poppr are also valuable to analysis of any populations. A manual with documentation and examples is provided. Poppr is open source and major releases are available on CRAN: http://cran.r-project.org/package=poppr. More supporting documentation and tutorials can be found under ‘resources’ at: http://grunwaldlab.cgrb.oregonstate.edu/.
1,942 citations
National University of Cordoba1, Max Planck Society2, VU University Amsterdam3, Macquarie University4, University of Grenoble5, University of Lyon6, Leipzig University7, Industrial University of Santander8, University of Oldenburg9, Imperial College London10, University of Montpellier11, Forschungszentrum Jülich12, University of Western Sydney13, University of Minnesota14, University of New South Wales15, Royal Botanic Gardens16, Missouri Botanical Garden17, George Washington University18, Paul Sabatier University19, Smithsonian Tropical Research Institute20, Komarov Botanical Institute21, Institut national de la recherche agronomique22, University of Bordeaux23, Florida International University24, University of Insubria25, University of Milan26, Université de Sherbrooke27, Centro Agronómico Tropical de Investigación y Enseñanza28
TL;DR: Analysis of worldwide variation in six major traits critical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled found that occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs.
Abstract: The authors found that the key elements of plant form and function, analysed at global scale, are largely concentrated into a two-dimensional plane indexed by the size of whole plants and organs on the one hand, and the construction costs for photosynthetic leaf area, on the other.
1,814 citations