ReviewAnnals of translational medicine2017
Insights into study design and statistical analyses in translational microbiome studies.
Review in Annals of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Changes in the Firmicutes to Bacteriodetes ratio in the gut microbiome in individuals with anorexia nervosa following inpatient treatment: A systematic review and a case series.Brain and behavior · 2024Pooled it
- Role of microbiota and microbiota-derived short-chain fatty acids in PDAC.Cancer medicine · 2023Review
- Association of upper airway bacterial microbiota and asthma: systematic review.Asia Pacific allergy · 2022Review
- ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study.Frontiers in genetics · 2022Article
- NFnetFu: A novel workflow for microbiome data fusion.Computers in biology and medicine · 2021Article
- Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls.Journal of biosciences · 2019Review
- Article
- Systematic Review of Gut Microbiota and Major Depression.Frontiers in psychiatry · 2019Article
- Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.Microbiome · 2018Article
- MDPbiome: microbiome engineering through prescriptive perturbations.Bioinformatics (Oxford, England) · 2018Article
- Human Microbiome Acquisition and Bioinformatic Challenges in Metagenomic Studies.International journal of molecular sciences · 2018Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research questions in translational microbiome studies are substantially more complex than their counterparts in basic science. Robust study designs with appropriate statistical analysis frameworks are pivotal to the success of these translational studies. This review considers how study designs can account for heterogeneous phenotypes by adopting representative sampling schemes for recruiting the study population and making careful choices about the control population. Advantages and limitations of 16S profiling and whole-genome sequencing, the two primary techniques for measuring the microbiome, are discussed followed by an overview of bioinformatic processing of high-throughput sequencing data from these measurements. Practical insights into the downstream statistical analyses including data processing and integration, variable transformations, and data exploration are provided. The merits of regularization and ensemble modeling for analyzing microbiome data are discussed along with a recommendation for selecting modeling approaches based on data-driven simulations and objective evaluation. The review builds on several recent discussions of study design issues in microbiome research but with a stronger emphasis on the downstream and often-ignored aspects of statistical analyses that are crucial for bridging the gap between basic science and translation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.