Evidence map›Paper›PMID 32746888›Full record

ArticleGenome biology2020

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data.

Matteo Calgaro, Chiara Romualdi, Levi Waldron, Davide Risso, Nicola Vitulo

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing 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

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

  1. Pooled it
  2. Gut Microbiota in Patients with Polycystic Ovary Syndrome: a Systematic Review.Reproductive sciences (Thousand Oaks, Calif.) · 2022
    Pooled it
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15 more citing papers are in PubMed but not listed here.

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

5 authors.

Matteo CalgaroDepartment of Biotechnology, University of Verona, Verona, Italy.
Chiara RomualdiDepartment of Biology, University of Padova, Padova, Italy.
Levi WaldronGraduate School of Public Health and Health Policy and Institute for Implementation Science in Public Health, City University of New York, New York, NY, USA.
Davide RissoDepartment of Statistical Sciences, University of Padova, Padova, Italy. davide.risso@unipd.it.ORCID 0000-0001-8508-5012
Nicola VituloDepartment of Biotechnology, University of Verona, Verona, Italy. nicola.vitulo@univr.it.

Funding

Cancer Genomics:Integrative and Scalable Solutions in R / BioconductorU24CA180996 · NCI · ROSWELL PARK CANCER INSTITUTE CORP · PI MORGAN, MARTIN T, WALDRON, LEVI · 2014 to 2023
$6.9M
Exploiting Public Metagenomic Data to Uncover Cancer-Microbiome RelationshipsR01CA230551 · NCI · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI Levi Waldron · 2020 to 2026
$3.9M
Exploiting public metagenomic data to uncover cancer-microbiome relationshipsU01CA230551 · NCI · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI WALDRON, LEVI · 2019 to 2019
$464k
NCI NIH HHS 1U01CA230551NCI NIH HHS 2U24CA180996NCI NIH HHS R01 CA230551NCI NIH HHS U01 CA230551NCI NIH HHS U24 CA180996
6 · The paper itself

Abstract

backgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.

resultsWe compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, power, and correct identification of differentially abundant genera. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.

conclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

Indexed as

MicrobiotaStatistics as TopicHumansMetagenomicsSequence Analysis, RNASingle-Cell AnalysisBenchmarkDifferential abundanceMetagenomicsMicrobiomeSingle-cell

Identifiers

PMID32746888
PMCPMC7398076

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.