ArticleNAR genomics and bioinformatics2020
Shrinkage improves estimation of microbial associations under different normalization methods.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identifying correlations driven by influential observations in large datasets.Briefings in bioinformatics · 2022Pooled it
- Impact of extended fertilization on soil microbial diversity: a comparative full-length 16S rRNA amplicon analysis.Genomics & informatics · 2026Article
- A New Framework to Empower Ecosystem Assessment Through the Integration of eDNA Inventories, Graph Theory and Niche Modelling.Ecology letters · 2026Article
- MNetClass: a control-free microbial network clustering framework for identifying central subcommunities across ecological niches.mSystems · 2025Article
- Human limits in machine learning: prediction of potato yield and disease using soil microbiome data.BMC bioinformatics · 2024Article
- Microbiome-based classification models for fresh produce safety and quality evaluation.Microbiology spectrum · 2024Article
- A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data.Frontiers in microbiology · 2024Article
- Poisson hurdle model-based method for clustering microbiome features.Bioinformatics (Oxford, England) · 2023Article
- Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.PLoS computational biology · 2023Article
- Is There a Universal Endurance Microbiota?Microorganisms · 2022Article
- How much does the typical ecological meta-analysis overestimate the true mean effect size?Ecology and evolution · 2022Article
- Negative binomial factor regression with application to microbiome data analysis.Statistics in medicine · 2022Article
- Adaptive and powerful microbiome multivariate association analysis via feature selection.NAR genomics and bioinformatics · 2022Article
- Bacterial low-abundant taxa are key determinants of a healthy airway metagenome in the early years of human life.Computational and structural biotechnology journal · 2022Article
- Metagenomic study of the gut microbiota associated with cow milk consumption in Chinese peri-/postmenopausal women.Frontiers in microbiology · 2022Article
- NetCoMi: network construction and comparison for microbiome data in R.Briefings in bioinformatics · 2021Article
- Tree-aggregated predictive modeling of microbiome data.Scientific reports · 2021Article
- Editorial: Compositional data analysis and related methods applied to genomics-a first special issue fromNAR genomics and bioinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimation of statistical associations in microbial genomic survey count data is fundamental to microbiome research. Experimental limitations, including count compositionality, low sample sizes and technical variability, obstruct standard application of association measures and require data normalization prior to statistical estimation. Here, we investigate the interplay between data normalization, microbial association estimation and available sample size by leveraging the large-scale American Gut Project (AGP) survey data. We analyze the statistical properties of two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis workflows and log-ratio transformations. We show that shrinkage estimation, a standard statistical regularization technique, can universally improve the quality of taxon-taxon association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as downstream data analysis examples, we show that variance-stabilizing and log-ratio approaches enable the most taxonomically and structurally coherent estimates. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.
Identifiers
What OpenQuestion holds
Registered trials
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.