Both a means of denoising and simplification, it can be beneficial for the majority of modern biological datasets, in which it’s not uncommon to have hundreds or even millions of simultaneous ...measurements collected for a single sample. Because of “the curse of dimensionality,” many statistical methods lack power when applied to high-dimensional data. Formally, the Marchenko–Pastur distribution asymptotically models the distribution of the singular values of large random matrices. ...for datasets large in both the number of observations and features, you use a rule of retaining only eigenvalues outside the support of the fitted Marchenko–Pastur distribution; however, remember that this applies only when your data have at least thousands of samples and thousands of features. ...the height-to-width ratio of a PCA plot should be consistent with the ratio between the corresponding eigenvalues. Because eigenvalues reflect the variance in coordinates of the associated PCs, you only need to ensure that in the plots, one "unit" in direction of one PC has the same length as one "unit" in direction of another PC. Because batch effects can confound the signal of interest, it is a good practice to check for their presence and, if found, to remove them before proceeding with further downstream analysis.
Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters ...of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs-including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction-and of de novo OTUs-including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.
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.
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.
The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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.
We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and ...resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants-DNA sequences not truly present in the sample. Contaminants ...come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls.
Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants.
Decontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.
In a series of studies of the gut microbiome, "enterotypes" have been used to classify gut microbiome samples that cluster together in ordination analyses. Initially, three distinct enterotypes were ...described, although later studies reduced this to two clusters, one dominated by Bacteroides or Clostridiales species found more commonly in Western (American and Western European) subjects and the other dominated by Prevotella more often associated with non-Western subjects. The two taxa, Bacteroides and Prevotella, have been presumed to represent consistent underlying microbial communities, but no one has demonstrated the presence of additional microbial taxa across studies that can define these communities.
We analyzed the combined microbiome data from five previous studies with samples across five continents. We clearly demonstrate that there are no consistent bacterial taxa associated with either Bacteroides- or Prevotella-dominated communities across the studies. By increasing the number and diversity of samples, we found gradients of both Bacteroides and Prevotella and a lack of the distinct clusters in the principal coordinate plots originally proposed in the "enterotypes" hypothesis. The apparent segregation of the samples seen in many ordination plots is due to the differences in the samples' Prevotella and Bacteroides abundances and does not represent consistent microbial communities within the "enterotypes" and is not associated with other taxa across studies. The projections we see are consistent with a continuum of values created from a simple mixture of Bacteroides and Prevotella; these two biomarkers are significantly correlated to the projection axes. We suggest that previous findings citing Bacteroides- and Prevotella-dominated clusters are the result of an artifact caused by the greater relative abundance of these two taxa over other taxa in the human gut and the sparsity of Prevotella abundant samples.
We believe that the term "enterotypes" is misleading because it implies both an underlying consistency of community taxa and a clear separation of sets of human gut samples, neither of which is supported by the broader data. We propose the use of "biomarker" as a more accurate description of these and other taxa that correlate with diet, lifestyle, and disease state.
The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical ...perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554-4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.
High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to ...quantify and compare abundance levels or microbial composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, including both parameteric and nonparametric methods. We provide examples of using the
R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests, partial least squares and linear models as well as nonparametric testing using community networks and the ggnetwork package.
The simultaneous study of multiple measurement types is a frequently encountered problem in practical data analysis. It is especially common in microbiome research, where several sources of data—for ...example, 16s-rRNA, metagenomic, metabolomic, or transcriptomic data–can be collected on the same physical samples. There has been a proliferation of proposals for analyzing such multitable microbiome data, as is often the case when new data sources become more readily available, facilitating inquiry into new types of scientific questions. However, stepping back from the rush for new methods for multitable analysis in the microbiome literature, it is worthwhile to recognize the broader landscape of multitable methods, as they have been relevant in problem domains ranging across economics, robotics, genomics, chemometrics, and neuroscience. In different contexts, these techniques are called data integration, multi-omic, and multitask methods, for example. Of course, there is no unique optimal algorithm to use across domains—different instances of the multitable problem possess specific structure or variation that are worth incorporating in methodology. Our purpose here is not to develop new algorithms, but rather to 1) distill relevant themes across different analysis approaches and 2) provide concrete workflows for approaching analysis, as a function of ultimate analysis goals and data characteristics (heterogeneity, dimensionality, sparsity). Towards the second goal, we have made code for all analysis and figures available online at
https://github.com/krisrs1128/multitable_review
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