Evidence map›Paper›PMID 35281829›Full record

ArticleFrontiers in genetics2022

ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study.

Linchen He, Chan Wang, Jiyuan Hu, Zhan Gao, Emilia Falcone, Steven M Holland, Martin J Blaser, Huilin Li

Abstract read
In one paragraph

Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

8 authors.

Linchen HeNovartis Pharmaceuticals Corporation, East Hanover, NJ, United States.
Chan WangDivision of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States.
Jiyuan HuDivision of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States.
Zhan GaoCenter for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, United States.
Emilia FalconeDivision of Intramural Research, Immunopathogenesis Section, NIAID, NIH, Bethesda, MD, United States.
Steven M HollandDivision of Intramural Research, Immunopathogenesis Section, NIAID, NIH, Bethesda, MD, United States.
Martin J BlaserCenter for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, United States.
Huilin LiDivision of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States.

Funding

Microbial, immune, metabolic perturbations by antibiotics (MIME study)U01AI122285 · NIAID · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI MARTIN J BLASER, Steven M Holland · 2016 to 2026
$5.2M
The Oral Microbiome and Upper Aerodigestive Squamous Cell CancerR01CA159036 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI AHN, JIYOUNG, HAYES, RICHARD BERNARD · 2011 to 2024
$3.5M
Project 2P20CA252728 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI AHN, JIYOUNG · 2020 to 2022
$3.4M
NCI NIH HHS P20 CA252728NCI NIH HHS R01 CA159036NIAID NIH HHS U01 AI122285
6 · The paper itself

Abstract

Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.

Indexed as

absolute abundanceautoregressivelongitudinal microbiome datamicrobial community stabilitymicrobial interactionmixed-effects modelnetwork analysiszero-inflated

Identifiers

PMID35281829
PMCPMC8914110

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.