Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA

Kelly, Brendan J; Groß, Robert; Bittinger, Kyle; Sherrill-Mix, Scott; Lewis, James D.; Collman, Ronald G.; Bushman, Frederic D.; Li, Hongzhe

doi:10.1093/bioinformatics/btv183

Cited by 328 publications

(281 citation statements)

References 24 publications

(25 reference statements)

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“…While sample size is always a valid concern, based on the microbiome-based power calculation, the current sample size is powered to detect a relatively large overall effect: 90 % power for an ω 2 = 0.04, unweighted UniFrac, an effect size similar to that of antibiotics [34]. Even at this small sample size, we were still able to identify significant microbiome differences between disease states and identify differential abundant taxa after multiple testing correction.…”

Section: Discussionmentioning

confidence: 99%

Potential contribution of the uterine microbiome in the development of endometrial cancer

et al. 2016

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BackgroundEndometrial cancer studies have led to a number of well-defined but mechanistically unconnected genetic and environmental risk factors. One of the emerging modulators between environmental triggers and genetic expression is the microbiome. We set out to inquire about the composition of the uterine microbiome and its putative role in endometrial cancer.MethodsWe undertook a study of the microbiome in samples taken from different locations along the female reproductive tract in patients with endometrial cancer (n = 17), patients with endometrial hyperplasia (endometrial cancer precursor, n = 4), and patients afflicted with benign uterine conditions (n = 10). Vaginal, cervical, Fallopian, ovarian, peritoneal, and urine samples were collected aseptically both in the operating room and the pathology laboratory. DNA extraction was followed by amplification and high-throughput next generation sequencing (MiSeq) of the 16S rDNA V3-V5 region to identify the microbiota present. Microbiota data were summarized using both α-diversity to reflect species richness and evenness within bacterial populations and β-diversity to reflect the shared diversity between bacterial populations. Statistical significance was determined through the use of multiple testing, including the generalized mixed-effects model.ResultsThe microbiome sequencing (16S rDNA V3-V5 region) revealed that the microbiomes of all organs (vagina, cervix, Fallopian tubes, and ovaries) are significantly correlated (p < 0.001) and that there is a structural microbiome shift in the cancer and hyperplasia cases, distinguishable from the benign cases (p = 0.01). Several taxa were found to be significantly enriched in samples belonging to the endometrial cancer cohort: Firmicutes (Anaerostipes, ph2, Dialister, Peptoniphilus, 1–68, Ruminococcus, and Anaerotruncus), Spirochaetes (Treponema), Actinobacteria (Atopobium), Bacteroidetes (Bacteroides and Porphyromonas), and Proteobacteria (Arthrospira). Of particular relevance, the simultaneous presence of Atopobium vaginae and an uncultured representative of the Porphyromonas sp. (99 % match to P. somerae) were found to be associated with disease status, especially if combined with a high vaginal pH (>4.5).ConclusionsOur results suggest that the detection of A. vaginae and the identified Porphyromonas sp. in the gynecologic tract combined with a high vaginal pH is statistically associated with the presence of endometrial cancer. Given the documented association of the identified microorganisms with other pathologies, these findings raise the possibility of a microbiome role in the manifestation, etiology, or progression of endometrial cancer that should be further investigated.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0368-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Potential contribution of the uterine microbiome in the development of endometrial cancer

et al. 2016

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“…This is a proof-of-principle pilot study to develop a novel methodology for quantitation of clinically important bacterial species and antibiotic resistance genes from fecal samples in lieu of screening cultures. Samples from patients with presumed high and low risk for MDR bacteria were selected at a ratio of 1:2 with controls for a sample size of 90, which can achieve a power of Ͼ99% with deep sequencing in this study at a type 1 error rate of 0.03 for metagenome analysis using the HMP package in R with corrections using the Bonferroni method (22)(23)(24). As all patient samples were deidentified and no clinical information was obtained in association with any sample, institutional board review and informed consent were not required.…”

Section: Methodsmentioning

confidence: 99%

Use of Shotgun Metagenome Sequencing To Detect Fecal Colonization with Multidrug-Resistant Bacteria in Children

et al. 2016

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Prevention of multidrug-resistant (MDR) bacterial infections relies on accurate detection of these organisms. We investigated shotgun metagenome sequencing for the detection of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and MDR Enterobacteriaceae. Fecal metagenomes were analyzed from high-risk inpatients and compared to those of low-risk outpatients and controls with minimal risk for a MDR bacterial infection. Principal-component analysis clustered patient samples into distinct cohorts, confirming that the microbiome composition was significantly different between cohorts (P ‫؍‬ 0.006). Microbial diversity and relative anaerobe abundance were preserved in outpatients compared to those in controls. Relative anaerobe abundance was significantly reduced in inpatients compared to that in outpatients (P ‫؍‬ 0.006). Although the potential for MDR bacteria was increased in inpatients and outpatients compared to that in controls (P < 0.001), there was no difference between inpatients and outpatients. However, 9 (53%) inpatients had colonization with a MDR bacterium that was not identified by culture. Unlike culture, shotgun sequencing quantitatively characterizes the burdens of multiple MDR bacteria relative to all of the microbiota within the intestinal community. We propose consideration of key microbiome features, such as diversity and relative anaerobe abundance, in addition to the detection of MDR bacteria by shotgun metagenome sequencing as a novel method that might better identify patients who are at increased risk of a MDR infection. Multidrug-resistant (MDR) bacterial infections cause significant morbidity and mortality and are increasingly common in children (1-3). Clinically important MDR bacteria include methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), and MDR Enterobacteriaceae with AmpC beta-lactamase resistance (AmpC), extended-spectrum beta-lactamase (ESBL) resistance, or carbapenem-resistant betalactamases (CRE). Antimicrobial exposures reduce the commensal gut anaerobes with compensatory increased Enterobacteriaceae and Enterococcus abundances as well as bacteriophages, allowing transmission of antibiotic resistance genes between species (4). This results in the loss of host resistance to abnormal fecal colonization and may select for MDR bacteria (5-7). Subsequently, the intestines serve as the primary reservoir of MDR bacteria (8, 9). Increased abundances of these organisms within the intestines precede the onset of an invasive bloodstream infection (BSI), and the risk of infection is related to the density of colonization (10-13).Timely and accurate detection of MDR bacteria is crucial to prevent the spread of MDR bacteria and to decrease rates of MDR infections (14). There is an urgent need for the development of new diagnostic methods, including metagenomics, to combat rising antibiotic resistance rates, which was explicitly stated in recent national action plans by the CDC and the White House (15, 16). D...

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“…These include the effect size [measure of observed differences caused by the factor(s)], the DNA sequencing depth, the level of taxonomical description, the choice of the test statistics, the critical value for the type I error probability, the method of multiple comparison correction, and the sample size (Hair et al, 2010;La Rosa et al, 2015). The dependence of power on some of these parameters is presented in Jonsson et al (2016), Kelly et al (2015) and La Rosa et al (2015). The dependence on sample size is especially important for metagenomic studies, as the high cost of sequencing is very restrictive.…”

Section: Basic Stepsmentioning

confidence: 99%

“…The latter parameter can be determined during a pilot experiment (when a small subgroup of metagenomes is sequenced) or from published data revealing typical effect sizes in similar experiments (such as Kelly et al, 2015;La Rosa et al, 2015). It is worth noting that it can possibly be overestimated in experiments with a small sample size (Button et al, 2013).…”

Section: Basic Stepsmentioning

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%

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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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Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA

Cited by 328 publications

References 24 publications

Potential contribution of the uterine microbiome in the development of endometrial cancer

Potential contribution of the uterine microbiome in the development of endometrial cancer

Use of Shotgun Metagenome Sequencing To Detect Fecal Colonization with Multidrug-Resistant Bacteria in Children

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

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