Evidence map›Paper›PMID 33622856›Full record

ArticlemSystems2021

Data Analysis Strategies for Microbiome Studies in Human Populations-a Systematic Review of Current Practice.

Sven Kleine Bardenhorst, Tom Berger, Frank Klawonn, Marius Vital, André Karch, Nicole Rübsamen

Abstract read
In one paragraph

Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  8. Review
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  15. Article
  16. Gut microbiota signatures in inflammatory bowel disease.United European gastroenterology journal · 2024
    Article
  17. Article
  18. Article
  19. Article
  20. Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples.International journal of environmental research and public health · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sven Kleine BardenhorstInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany s.kleinebardenhorst@uni-muenster.de.
Tom BergerInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Frank KlawonnResearch Group Biostatistics, Helmholtz Centre for Infection Research, Braunschweig, Germany.
Marius VitalInstitute of Medical Microbiology and Hospital Hygiene, Hannover Medical School, Hannover, Germany.
André KarchInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
Nicole RübsamenInstitute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reproducibility is a major issue in microbiome studies, which is partly caused by missing consensus about data analysis strategies. The complex nature of microbiome data, which are high-dimensional, zero-inflated, and compositional, makes them challenging to analyze, as they often violate assumptions of classic statistical methods. With advances in human microbiome research, research questions and study designs increase in complexity so that more sophisticated data analysis concepts are applied. To improve current practice of the analysis of microbiome studies, it is important to understand what kind of research questions are asked and which tools are used to answer these questions. We conducted a systematic literature review considering all publications focusing on the analysis of human microbiome data from June 2018 to June 2019. Of 1,444 studies screened, 419 fulfilled the inclusion criteria. Information about research questions, study designs, and analysis strategies were extracted. The results confirmed the expected shift to more advanced research questions, as one-third of the studies analyzed clustered data. Although heterogeneity in the methods used was found at any stage of the analysis process, it was largest for differential abundance testing. Especially if the underlying data structure was clustered, we identified a lack of use of methods that appropriately addressed the underlying data structure while taking into account additional dependencies in the data. Our results confirm considerable heterogeneity in analysis strategies among microbiome studies; increasingly complex research questions require better guidance for analysis strategies.

Indexed as

16S rRNAanalysis strategiesmicrobiomeshotgun metagenomics sequencing

Identifiers

PMID33622856
PMCPMC8573962

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.