Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data

Rosa, Patricio S. La; Brooks, J. Paul; Deych, Elena; Boone, Edward L.; Edwards, D.J.; Wang, Qin; Sodergren, Erica; Weinstock, George M.; Shannon, William D.

doi:10.1371/journal.pone.0052078

Cited by 272 publications

(280 citation statements)

References 28 publications

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“…24 We examined our data for statistical power using the included analysis tools (Table S2). All treatment groups could be differentiated when taxonomic data are included as part of the QIIME diversity analysis.…”

Section: Discussionmentioning

confidence: 99%

Impact of probiotic supplements on microbiome diversity following antibiotic treatment of mice

2016

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Shifts in microbial populations of the intestinal tract have been associated with a multitude of nutritional, autoimmune, and infectious diseases. The limited diversity following antibiotic treatments creates a window for opportunistic pathogens, diarrhea, and inflammation as the microbiome repopulates. Depending on the antibiotics used, microbial diversity can take weeks to months to recover. To alleviate this loss of diversity in the intestinal microbiota, supplementation with probiotics has become increasingly popular. However, our understanding of the purported health benefits of these probiotic bacteria and their ability to shape the microbiome is significantly lacking. This study examined the impact of probiotics concurrent with antibiotic treatment or during the recovery phase following antibiotic treatment of mice. We found that probiotics did not appear to colonize the intestine themselves or shift the overall diversity of the intestinal microbiota. However, the probiotic supplementation did significantly change the types of bacteria which were present. In particular, during the recovery phase the probiotic caused a suppression of Enterobacteriaceae outgrowth (Shigella and Escherichia) while promoting a blooming of Firmicutes, particularly from the Anaerotruncus genus. These results indicate that probiotics have a significant capacity to remodel the microbiome of an individual recovering from antibiotic therapy.

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Section: Discussionmentioning

confidence: 99%

Impact of probiotic supplements on microbiome diversity following antibiotic treatment of mice

2016

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“…Therefore, at the stage of experiment planning it is prudent to ensure that the selected sample size is sufficient to detect the underlying dependences but is not redundant and that the sequencing depth allows to capture the minor community members at the desired level of detail. In practice, 80% power threshold is commonly used in biostatistical analysis (La Rosa et al, 2012). Details on the choice of power threshold are described elsewhere (Sham and Purcell, 2014).…”

Section: Basic Stepsmentioning

confidence: 99%

“…However, their flaw is relatively low sensitivity, which leads to the inflated rate of type II error probability (La Rosa et al, 2012).…”

Section: Total Read Count Varies Between the Samplesmentioning

confidence: 99%

“…Here we will describe R packages specifically intended for metagenomic analysis: basic methods commonly used for a comparison of two or more groups [on the example of their implementation in ALDEx2 package (Fernandes et al, 2014)] and advanced approaches based on generalized linear models allowing both continuous and discrete factors [metagenomeSeq (Paulson et al, 2013), edgeR (McCarthy et al, 2012), DESeq2 (Love et al, 2014), MaAsLin, shotgunFunctionalizeR (Kristiansson et al, 2009)]. Finally, the methods for vector-wise rather than component-wise comparison will be introduced [HMP (La Rosa et al, 2012), vegan (Oksanen et al, 2012), micropower (Kelly et al, 2015)]. The summary of all mentioned packages is presented in Table 2.2.…”

Section: Total Read Count Varies Between the Samplesmentioning

confidence: 99%

“…The Dirichlet-multinomial distribution is able to avoid this flaw, as it provides an overdispersion parameter that can be derived independently of the data; in the case of the zero overdispersion it coincides with the multinomial distribution. It is shown in a paper by La Rosa et al (2012) that this approach is indeed better to describe the metagenomic data.…”

Section: Community-level Comparison Of Microbial Communitiesmentioning

confidence: 99%

See 2 more Smart Citations

Guidelines to Statistical Analysis of Microbial Composition Data Inferred from Metagenomic Sequencing

Odintsova¹,

Tyakht²,

Alexeev³

2017

Current Issues in Molecular Biology

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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. IntroductionMicrobiota, 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

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Statistical and Computational Methods in Microbiome and Metagenomics

2019

Handbook of Statistical Genomics

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Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data

Cited by 272 publications

References 28 publications

Impact of probiotic supplements on microbiome diversity following antibiotic treatment of mice

Impact of probiotic supplements on microbiome diversity following antibiotic treatment of mice

Guidelines to Statistical Analysis of Microbial Composition Data Inferred from Metagenomic Sequencing

Statistical and Computational Methods in Microbiome and Metagenomics

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