ArticleStatistics in biosciences2018
Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data.
Article in Statistics in biosciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 31 citations in OpenAlex.
- Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (ZINQ).Microbiome · 2021Article
- A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.Biostatistics (Oxford, England) · 2021Article
- mbImpute: an accurate and robust imputation method for microbiome data.Genome biology · 2021Article
- Long-term dietary intake from infancy to late adolescence is associated with gut microbiota composition in young adulthood.The American journal of clinical nutrition · 2021Article
- MB-GAN: Microbiome Simulation via Generative Adversarial Network.GigaScience · 2021Article
- IFAA: Robust Association Identification and Inference for Absolute Abundance in Microbiome Analyses.Journal of the American Statistical Association · 2021Article
- Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows.The ISME journal · 2020Article
- Nutrient-toxic element mixtures and the early postnatal gut microbiome in a United States longitudinal birth cohort.Environment international · 2020Article
- MODELING MICROBIAL ABUNDANCES AND DYSBIOSIS WITH BETA-BINOMIAL REGRESSION.The annals of applied statistics · 2020Article
- Specific class of intrapartum antibiotics relates to maturation of the infant gut microbiota: a prospective cohort study.BJOG : an international journal of obstetrics and gynaecology · 2020Article
- Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites.Frontiers in genetics · 2019Article
- Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.Microbiome · 2018Article
- Sex-specific associations of infants' gut microbiome with arsenic exposure in a US population.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
The human microbiome plays critical roles in human health and has been linked to many diseases. While advanced sequencing technologies can characterize the composition of the microbiome in unprecedented detail, it remains challenging to disentangle the complex interplay between human microbiome and disease risk factors due to the complicated nature of microbiome data. Excessive numbers e f zero values, high dimensionality, the hierarchical phylogenetic tree and compositional structure are compounded and consequently make existing methods inadequate to appropriately address these issues. We propose a multivariate two-part zero-inflated logistic normal (MZILN) model to analyze the association of disease risk factors with individual microbial taxa and overall microbial community composition. This approach can naturally handle excessive numbers e f zeros and the compositional data structure with the discrete part and the logistic-normal part e f the model. For parameter estimation, an estimating equations approach is employed that enables us to address the complex inter-taxa correlation structure induced by the hierarchical phylogenetic tree structure and the compositional data structure. This model is able to incorporate standard regularization approaches to deal with high dimensionality. Simulation shews that our model outperforms existing methods. Our approach is also compared to ethers using the analysis of real microbiome data.
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.