Evidence map›Paper›PMID 35421994›Full record

ArticleGenome biology2022

LinDA: linear models for differential abundance analysis of microbiome compositional data.

Huijuan Zhou, Kejun He, Jun Chen, Xianyang Zhang

Abstract read
In one paragraph

Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 267 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
267citing papers in PubMed, 1 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

267 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Effect of pasteurizedGut microbes · 2026
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  16. Oncoimmunology · 2026
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207 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

4 authors.

Huijuan ZhouShanghai University of Finance and Economics, Shanghai, 200437, China.
Kejun HeRenmin University of China, Beijing, 100872, China.
Jun ChenMayo Clinic, Rochester, USA. chen.jun2@mayo.edu.
Xianyang ZhangTexas A&M University, College Station, 77843, USA. zhangxiany@stat.tamu.edu.

Funding

Methods for microbiome compositional dataR01GM144351 · NIGMS · MAYO CLINIC ROCHESTER · PI Jun Chen, Xianyang Zhang · 2022 to 2026
$1.6M
NIGMS NIH HHS R01 GM144351NIGMS NIH HHS R01GM144351
6 · The paper itself

Abstract

Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed by a simple, yet highly flexible and scalable, approach. The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio transformed data, and correcting the bias due to compositional effects. We show that LinDA enjoys asymptotic FDR control and can be extended to mixed-effect models for correlated microbiome data. Using simulations and real examples, we demonstrate the effectiveness of LinDA.

Indexed as

ColeopteraMicrobiotaAnimalsLinear ModelsResearch DesignCompositional effectDifferential abundance analysisFalse discovery rateMultiple testing

Identifiers

PMID35421994
PMCPMC9012043

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.