Reproducibility: The Search for Microbiome Standards

Martin, Joseph

doi:10.2144/btn-2019-0096

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Most microbiome studies to date have been underpowered when considering the substantial temporal and inter-individual variability of host-associated microbial communities. Rapid progress in the laboratory and computational methods used to study microbiomes means that any two microbiome studies likely used measurement protocols different enough to make their results quantitatively incomparable (Martin, 2019; McLaren et al, 2019; Tierney et al, 2022). The interaction between microbiomes and the host is mediated by poorly understood, and hence largely unrecorded, environmental and individual factors.…”

Section: Discussionmentioning

confidence: 99%

Meta-Analysis Reveals the Vaginal Microbiome is a Better Predictor of Earlier Than Later Preterm Birth

Huang

Gin

Fettweis

et al. 2022

Preprint

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High-throughput sequencing measurements of the vaginal microbiome have yielded intriguing potential relationships between the vaginal microbiome and preterm birth (PTB; live birth prior to 37 weeks of gestation). However, results across studies have been inconsistent. Here we perform an integrated analysis of previously published datasets from 12 cohorts of pregnant women whose vaginal microbiomes were measured by 16S rRNA gene sequencing. Of 1926 women included in our analysis, 568 went on to deliver prematurely. Substantial variation between these datasets existed in their definition of preterm birth, characteristics of the study populations, and sequencing methodology. Nevertheless, a small group of taxa comprised a vast majority of the measured microbiome in all cohorts. We trained machine learning (ML) models to predict PTB from the composition of the vaginal microbiome, finding low to modest predictive accuracy (0.28-0.79). Predictive accuracy was typically lower when ML models trained in one dataset predicted PTB in another dataset. Earlier preterm birth (<32 weeks, <34 weeks) was more predictable from the vaginal microbiome than late preterm birth (34 - 37 weeks), both within and across datasets. Integrated differential abundance analysis revealed a highly significant negative association between L. crispatus and PTB that was consistent across almost all studies. The presence of the majority (18 out of 25) of genera was associated with a higher risk of PTB, with L. iners, Prevotella, and Gardnerella showing particularly consistent and significant associations. Some example discrepancies between studies could be attributed to specific methodological differences, but not most study-to-study variations in the relationship between the vaginal microbiome and preterm birth. We believe future studies of the vaginal microbiome and PTB will benefit from a focus on earlier preterm births, and improved reporting of specific patient metadata shown to influence the vaginal microbiome and/or birth outcomes.

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Section: Discussionmentioning

confidence: 99%

Meta-Analysis Reveals the Vaginal Microbiome is a Better Predictor of Earlier Than Later Preterm Birth

Huang

Gin

Fettweis

et al. 2022

Preprint

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“…The advent of readily available, low-cost high throughput DNA sequencing has promoted the rampant growth of microbiome and metagenome studies. These studies, while informative and insightful, are often fraught by well-meaning but ultimately incorrect data assumptions and statistical test applications (Martin, 2019;Free, 2020). The selection of statistical tests requires knowledge of underlying data characteristics such as sample distribution, sample size and equivariance (Figure 1), which can be easily misinterpreted especially for complex studies with a lot of groups and relatively small sample sizes per group (Makin and De Xivry, 2019;Konietschke, Schwab and Pauly, 2021).…”

Section: Introductionmentioning

confidence: 99%

statSuma: automated selection and performance of statistical comparisons for microbiome studies

Leigh

Murphy

Walsh

2021

Preprint

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There is a reproducibility crisis in scientific studies. Some of these crises arise from incorrect application of statistical tests to data that follow inappropriate distributions, have inconsistent equivariance, or have very small sample sizes. As determining which test is most appropriate for all data in a multicategorical study (such as comparing taxa between sites in microbiome studies), we present statsSuma, an interactive Python notebook (which can be run from any desktop computer using the Google Colaboratory web service) and does not require a user to have any programming experience. This software assesses underlying data structures in a given dataset to advise what pairwise or listwise statistical procedure would be best suited for all data. As some users may be interested in further mining specific trends, statSuma performs 5 different two-tailed pairwise tests (Student's t-test, Welch's t-test, Mann-Whitney U-test, Brunner-Munzel test, and a pairwise Kruskal-Wallis H-test) and advises the best test for each comparison. This software also advises whether ANOVA or a multicategorical Kruskal-Wallis H-test is most appropriate for a given dataset and performs both procedures. A data distribution-vs-Gaussian distribution plot is produced for each taxon at each site and a variance plot between all combinations of 2 taxa at each site are produced so Gaussian tests and variance tests can be visually confirmed alongside associated statistical determinants.

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“…A ‘replication crisis’ and ‘reproducibility crisis’ in natural sciences have been under intensified discussion since recently ( 1 , 2 , 3 ) and address the paradigm that scientists should be enabled to better repeat study setups and reproduce study results in the future. Particularly in life sciences including ecology, there is, for several reasons, a lack of empirical studies, which tested earlier research findings by repetition ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results

et al. 2020

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Repeatability of study setups and reproducibility of research results by underlying data are major requirements in science. Until now, abstract models for describing the structural logic of studies in environmental sciences are lacking and tools for data management are insufficient. Mandatory for repeatability and reproducibility is the use of sophisticated data management solutions going beyond data file sharing. Particularly, it implies maintenance of coherent data along workflows. Design data concern elements from elementary domains of operations being transformation, measurement and transaction. Operation design elements and method information are specified for each consecutive workflow segment from field to laboratory campaigns. The strict linkage of operation design element values, operation values and objects is essential. For enabling coherence of corresponding objects along consecutive workflow segments, the assignment of unique identifiers and the specification of their relations are mandatory. The abstract model presented here addresses these aspects, and the software DiversityDescriptions (DWB-DD) facilitates the management of thusly connected digital data objects and structures. DWB-DD allows for an individual specification of operation design elements and their linking to objects. Two workflow design use cases, one for DNA barcoding and another for cultivation of fungal isolates, are given. To publish those structured data, standard schema mapping and XML-provision of digital objects are essential. Schemas useful for this mapping include the Ecological Markup Language, the Schema for Meta-omics Data of Collection Objects and the Standard for Structured Descriptive Data. Data pipelines with DWB-DD include the mapping and conversion between schemas and functions for data publishing and archiving according to the Open Archival Information System standard. The setting allows for repeatability of study setups, reproducibility of study results and for supporting work groups to structure and maintain their data from the beginning of a study. The theory of ‘FAIR++’ digital objects is introduced.

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Reproducibility: The Search for Microbiome Standards

Abstract: The reproducibility crisis is resulting in a lot of discussion in various scientific fields. This feature explores the latest technologies, methods and projects relevant to the biological laboratory and aiming to solve this problem.

Cited by 5 publications

References 4 publications

Meta-Analysis Reveals the Vaginal Microbiome is a Better Predictor of Earlier Than Later Preterm Birth

Meta-Analysis Reveals the Vaginal Microbiome is a Better Predictor of Earlier Than Later Preterm Birth

statSuma: automated selection and performance of statistical comparisons for microbiome studies

FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results

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