scholarly journals metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effect models

2018 ◽  
Author(s):  
Nhan Thi Ho ◽  
Fan Li
Keyword(s):  
Random Effects ◽  
High Throughput ◽  
Relative Abundance ◽  
High Throughput Sequencing ◽  
Meta Analysis ◽  
R Package ◽  
Standard Errors ◽  
Relative Abundances ◽  
Meta Analyses ◽  
Analysis Models

ABSTRACTBackgroundThe rapid growth of high-throughput sequencing-based microbiome profiling has yielded tremendous insights into human health and physiology. Data generated from high-throughput sequencing of 16S rRNA gene amplicons are often preprocessed into composition or relative abundance. However, reproducibility has been lacking due to the myriad of different experimental and computational approaches taken in these studies. Microbiome studies may report varying results on the same topic, therefore, meta-analyses examining different microbiome studies to provide robust results are important. So far, there is still a lack of implemented methods to properly examine differential relative abundances of microbial taxonomies and to perform meta-analysis examining the heterogeneity and overall effects across microbiome studies.ResultsWe developed an R package ‘metamicrobiomeR’ that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a zero-inflated beta (BEZI) family (GAMLSS-BEZI) for analysis of microbiome relative abundance datasets. Both simulation studies and application to real microbiome data demonstrate that GAMLSS-BEZI well performs in testing differential relative abundances of microbial taxonomies. Importantly, the estimates from GAMLSS-BEZI are log(odds ratio) of relative abundances between groups and thus are comparable between microbiome studies. As such, we also apply random effects meta-analysis models to pool estimates and their standard errors across microbiome studies. We demonstrate the meta-analysis workflow and highlight the utility of our package on four studies comparing gut microbiomes between male and female infants in the first six months of life.ConclusionsGAMLSS-BEZI allows proper examination of microbiome relative abundance data. Random effects meta-analysis models can be directly applied to pool comparable estimates and their standard errors to evaluate the heterogeneity and overall effects across microbiome studies. The examples and workflow using our metamicrobiomeR package are reproducible and applicable for the analyses and meta-analyses of other microbiome studies.

Meta-Analysis of Meta-Analyses in Communication: Comparing Fixed Effects and Random Effects Analysis Models

2010 ◽  
Vol 58 (3) ◽  
pp. 257-278 ◽  
Author(s):  
Ashley Anker ◽  
Amber Marie Reinhart ◽  
Thomas Hugh Feeley
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Meta Analyses ◽  
Analysis Models

How to interpret meta-analysis models: fixed effect and random effects meta-analyses

2014 ◽  
Vol 17 (2) ◽  
pp. 64-64 ◽  
Author(s):  
Adriani Nikolakopoulou ◽  
Dimitris Mavridis ◽  
Georgia Salanti
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Fixed Effect ◽  
Meta Analyses ◽  
Analysis Models

A Review of Meta-Analysis Packages in R

2016 ◽  
Vol 42 (2) ◽  
pp. 206-242 ◽  
Author(s):  
Joshua R. Polanin ◽  
Emily A. Hennessy ◽  
Emily E. Tanner-Smith
Keyword(s):  
Open Source ◽  
Meta Analysis ◽  
R Package ◽  
Statistical Technique ◽  
Effect Sizes ◽  
Online Search ◽  
Multiple Primary ◽  
R Packages ◽  
Meta Analyses ◽  
Analysis Models

Meta-analysis is a statistical technique that allows an analyst to synthesize effect sizes from multiple primary studies. To estimate meta-analysis models, the open-source statistical environment R is quickly becoming a popular choice. The meta-analytic community has contributed to this growth by developing numerous packages specific to meta-analysis. The purpose of this study is to locate all publicly available meta-analytic R packages. We located 63 packages via a comprehensive online search. To help elucidate these functionalities to the field, we describe each of the packages, recommend applications for researchers interested in using R for meta-analyses, provide a brief tutorial of two meta-analysis packages, and make suggestions for future meta-analytic R package creators.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

scholarly journals Dynamics of Diversity and Abundance of Sulfonamide Resistant Bacteria in a Silt Loam Soil Fertilized by Compost

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 699
Author(s):  
Hui Han ◽  
Mohan Bai ◽  
Yanting Chen ◽  
Yali Gong ◽  
Ming Wu ◽  
...  
Keyword(s):  
High Throughput ◽  
Relative Abundance ◽  
High Throughput Sequencing ◽  
Resistant Bacteria ◽  
Arable Soils ◽  
Soil Bacterial Communities ◽  
Sulfonamide Resistance ◽  
Loam Soil ◽  
Abundance And Diversity ◽  
Sulfonamide Resistance Genes

Although composting is effective in deactivating antibiotic substances in manure, the influence of compost fertilization on the occurrence and dissemination of antibiotic resistance in arable soils remains to be controversial. Herein, the abundance and diversity of two sulfonamide resistance genes (sul1 and sul2) in soil fertilized by compost spiked with two concentrations of sulfadiazine (1 and 10 mg kg−1) were studied intensively by qPCR and high throughput sequencing based on a two-month microcosm experiment. The concentration of sulfadiazine decreased rapidly after spiking from 25% at Day 1 to less than 2.7% at Day 60. Relative abundance of both sul1 and sul2 were significantly higher in soil amended with compost than the non-amended control at Day 1 and slightly decreased with incubation time except for sul2 in the S10 treatment. Soil bacterial communities were transiently shifted by compost fertilization regardless of the presence of sulfadiazine. Relative abundance of genera in three hubs positively interlinked with sul1 and sul2 were significantly higher in compost treated soil than the control at Day 1, 7 and 21, but not at Day 60. High throughput sequencing analyses revealed that most detected (>67% in relative abundance) sul1 and sul2 genotypes sharing >99% similarity with those found in gammaproteobacterial pathogens frequently were commonly present in compost and soil. These results indicated that compost fertilization might increase the abundance rather than diversity of sulfadiazine-resistant populations in soil, which may be facilitated by the presence of sulfadiazine.

scholarly journals kataegis: an R package for identification and visualization of the genomic localized hypermutation regions using high-throughput sequencing

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xue Lin ◽  
Yingying Hua ◽  
Shuanglin Gu ◽  
Li Lv ◽  
Xingyu Li ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Somatic Mutations ◽  
R Package ◽  
Frequency Of Occurrence ◽  
Link Type ◽  
Genomic Landscape ◽  
One Step ◽  
Flanking Regions

Abstract Background Genomic localized hypermutation regions were found in cancers, which were reported to be related to the prognosis of cancers. This genomic localized hypermutation is quite different from the usual somatic mutations in the frequency of occurrence and genomic density. It is like a mutations “violent storm”, which is just what the Greek word “kataegis” means. Results There are needs for a light-weighted and simple-to-use toolkit to identify and visualize the localized hypermutation regions in genome. Thus we developed the R package “kataegis” to meet these needs. The package used only three steps to identify the genomic hypermutation regions, i.e., i) read in the variation files in standard formats; ii) calculate the inter-mutational distances; iii) identify the hypermutation regions with appropriate parameters, and finally one step to visualize the nucleotide contents and spectra of both the foci and flanking regions, and the genomic landscape of these regions. Conclusions The kataegis package is available on Bionconductor/Github (https://github.com/flosalbizziae/kataegis), which provides a light-weighted and simple-to-use toolkit for quickly identifying and visualizing the genomic hypermuation regions.

scholarly journals Environmental Sequencing Provides Reasonable Estimates of the Relative Abundance of Specific Picoeukaryotes

2016 ◽  
Vol 82 (15) ◽  
pp. 4757-4766 ◽  
Author(s):  
Caterina R. Giner ◽  
Irene Forn ◽  
Sarah Romac ◽  
Ramiro Logares ◽  
Colomban de Vargas ◽  
...  
Keyword(s):  
Microbial Diversity ◽  
High Throughput ◽  
Relative Abundance ◽  
Ribosomal Dna ◽  
18S Rdna ◽  
High Throughput Sequencing ◽  
454 Sequencing ◽  
Epifluorescence Microscopy ◽  
Environmental Sequencing

ABSTRACTHigh-throughput sequencing (HTS) is revolutionizing environmental surveys of microbial diversity in the three domains of life by providing detailed information on which taxa are present in microbial assemblages. However, it is still unclear how the relative abundance of specific taxa gathered by HTS correlates with cell abundances. Here, we quantified the relative cell abundance of 6 picoeukaryotic taxa in 13 planktonic samples from 6 European coastal sites using epifluorescence microscopy on tyramide signal amplification-fluorescencein situhybridization preparations. These relative abundance values were then compared with HTS data obtained in three separate molecular surveys: 454 sequencing of the V4 region of the 18S ribosomal DNA (rDNA) using DNA and RNA extracts (DNA-V4 and cDNA-V4) and Illumina sequencing of the V9 region (cDNA-V9). The microscopic and molecular signals were generally correlated, indicating that a relative increase in specific 18S rDNA was the result of a large proportion of cells in the given taxa. Despite these positive correlations, the slopes often deviated from 1, precluding a direct translation of sequences to cells. Our data highlighted clear differences depending on the nucleic acid template or the 18S rDNA region targeted. Thus, the molecular signal obtained using cDNA templates was always closer to relative cell abundances, while the V4 and V9 regions gave better results depending on the taxa. Our data support the quantitative use of HTS data but warn about considering it as a direct proxy of cell abundances.IMPORTANCEDirect studies on marine picoeukaryotes by epifluorescence microscopy are problematic due to the lack of morphological features and due to the limited number and poor resolution of specific phylogenetic probes used in fluorescencein situhybridization (FISH) routines. As a consequence, there is an increasing use of molecular methods, including high-throughput sequencing (HTS), to study marine microbial diversity. HTS can provide a detailed picture of the taxa present in a community and can reveal diversity not evident using other methods, but it is still unclear what the meaning of the sequence abundance in a given taxon is. Our aim is to investigate the correspondence between the relative HTS signal and relative cell abundances in selected picoeukaryotic taxa. Environmental sequencing provides reasonable estimates of the relative abundance of specific taxa. Better results are obtained when using RNA extracts as the templates, while the region of 18S ribosomal DNA had different influences depending on the taxa assayed.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

Alternative Tendon Coaptations to the Pulvertaft Weave Technique: A Systematic Review and Meta-Analysis of Biomechanical Studies

Hand ◽  
2021 ◽  
pp. 155894472110432
Author(s):  
Emily M. Graham ◽  
Jeremie D. Oliver ◽  
Russell Hendrycks ◽  
Dino Maglic ◽  
Shaun D. Mendenhall
Keyword(s):  
Systematic Review ◽  
Yield Strength ◽  
Random Effects ◽  
Meta Analysis ◽  
Dichotomous Data ◽  
Inverse Variance ◽  
Significant Difference ◽  
Alternative Techniques ◽  
Meta Analyses

Background The Pulvertaft weave technique (PT) is frequently used during tendon repairs and transfers. However, this technique is associated with limitations. In this systematic review and meta-analysis, quantitative and qualitative analyses were performed on in vitro, biomechanical studies that compared the PT with alternative techniques. Methods Articles included for qualitative and/or qualitative analysis were identified following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies included in the meta-analysis were analyzed either as continuous data with inverse variance and random effects or as dichotomous data using a Mantel-Haenszel analysis assuming random effects to calculate an odds ratio. Results A comprehensive electronic search yielded 8 studies meeting inclusion criteria for meta-analysis. Two studies with a total of 65 tendon coaptations demonstrated no significant difference in strength between the PT and traditional side-to-side (STS) techniques ( P = .92). Two studies with a total of 43 tendon coaptations showed that the STS with 1 weave has a higher yield strength than the PT ( P = .03). Two studies with a total of 62 tendon repairs demonstrated no significant difference in strength between the PT and the step-cut (SC) techniques ( P = .70). The final 2 studies included 46 tendon repairs and demonstrated that the wrap around (WA) technique has a higher yield strength than the PT ( P < .001). Conclusions The STS, SC, and WA techniques are preferred for improving tendon form. The STS and WA techniques have superior yield strengths than the PT, and the SC technique withstands similar stress to failure as the PT.

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