Evidence map›Paper›PMID 42243959›Full record

ArticleGenome biology2026

TestNet: a method for inferring microbial networks with false discovery rate control for clustered and unclustered samples.

Chang Su, Yicong Mao, Mengyu He, Vanessa E Van Doren, Colleen F Kelley, Yi-Juan Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Chang SuDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, USA.
Yicong MaoDepartment of Biostatistics, School of Public Health, Peking University, Beijing, China.
Mengyu HeDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, USA.
Vanessa E Van DorenThe Hope Clinic of the Emory Vaccine Research Center, Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Decatur, USA.
Colleen F KelleyThe Hope Clinic of the Emory Vaccine Research Center, Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Decatur, USA.
Yi-Juan HuDepartment of Biostatistics, School of Public Health, Peking University, Beijing, China. yijuanhu@bicmr.pku.edu.cn.

Funding

Novel Statistical Methods for Analyzing Complex Microbiome DataR01GM141074 · NIGMS · EMORY UNIVERSITY · PI SU, CHANG · 2021 to 2024
$1.2M
NIGMS NIH HHS R01 GM141074NIH HHS R01GM141074
6 · The paper itself

Abstract

Most existing methods for inferring microbial networks generate only point estimates of Pearson's correlations without assessing their significance, and none accounts for clustering. We introduce TestNet, a novel method that delivers well-calibrated results by controlling the false discovery rate (FDR). TestNet uses a permutation-based procedure to generate valid null replicates that account for compositional effects, excess zeros in microbiome data, and clustering within samples when present. Our results demonstrate that TestNet is the only evaluated method that effectively controls the FDR while maintaining high power across a wide range of scenarios.

Indexed as

MicrobiotaSoftwareAlgorithmsCluster AnalysisCo-abundanceLog ratioMicrobiomeNonlinear dependenceOmnibus test

Identifiers

PMID42243959
PMCPMC13455190

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Registered trials

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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.