Evidence map›Paper›PMID 30935409›Full record

ArticleMicrobiome2019

W

Bashir Hamidi, Kristin Wallace, Chenthamarakshan Vasu, Alexander V Alekseyenko

Abstract read
In one paragraph

Article in Microbiome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–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

36 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Consumer resilience suppresses the recovery of overgrazed ecosystems.Ecological applications : a publication of the Ecological Society of America · 2026
    Article
  5. Frontiers in microbiology · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Bashir HamidiProgram for Human Microbiome Research, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, 29425, SC, USA.
Kristin WallaceDepartment of Public Health Science, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, 29425, SC, USA.
Chenthamarakshan VasuDepartment of Microbiology and Immunology, Medical University of South Carolina, 173 Ashley Avenue MSC 509, Charleston, 29425, SC, USA.
Alexander V AlekseyenkoProgram for Human Microbiome Research, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, 29425, SC, USA. alekseye@musc.edu.ORCID 0000-0002-5748-2085

Funding

South Carolina Clinical & Translational Research Institute (SCTR)UL1TR001450 · NCATS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI BRADY, KATHLEEN T., FLUME, PATRICK A · 2015 to 2024
$41.1M
South Carolina Cancer Disparities Research Center (SC CADRE)U54CA210962 · NCI · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI TURNER, DAVID PAUL · 2017 to 2023
$6.7M
South Carolina Cancer Disparities Research Center (SC CaDRe)U54CA210963 · NCI · SOUTH CAROLINA STATE UNIVERSITY · PI FORD, MARVELLA ELIZABETH, SALLEY, JUDITH · 2017 to 2023
$6.1M
Distance-based Panomic Analytics for Microbiome DataR01LM012517 · NLM · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI ALEKSEYENKO, ALEXANDER V · 2018 to 2021
$1.3M
NCATS NIH HHS UL1 TR001450NCI NIH HHS U54 CA210962NCI NIH HHS U54 CA210963NLM NIH HHS R01 LM012517
6 · The paper itself

Abstract

backgroundCommunity-wide analyses provide an essential means for evaluation of the effect of interventions or design variables on the composition of the microbiome. Applications of these analyses are omnipresent in microbiome literature, yet some of their statistical properties have not been tested for robustness towards common features of microbiome data. Recently, it has been reported that PERMANOVA can yield wrong results in the presence of heteroscedasticity and unbalanced sample sizes.

findingsWe develop a method for multivariate analysis of variance, [Formula: see text], based on Welch MANOVA that is robust to heteroscedasticity in the data. We do so by extending a previously reported method that does the same for two-level independent factor variables. Our approach can accommodate multi-level factors, stratification, and multiple post hoc testing scenarios. An R language implementation of the method is available at https://github.com/alekseyenko/WdStar .

conclusionOur method resolves potential for confounding of location and dispersion effects in multivariate analyses by explicitly accounting for the differences in multivariate dispersion in the data tested. The methods based on [Formula: see text] have general applicability in microbiome and other 'omics data analyses.

Indexed as

MicrobiotaComputational BiologyComputer SimulationHumansMultivariate AnalysisSoftwareDistance MANOVAHeteroscedastic testWelch MANOVA

Identifiers

PMID30935409
PMCPMC6444669

What OpenQuestion holds

Textmetadata
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.