Dataset Information

GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data.

ABSTRACT: Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero-inflation remain largely undeveloped. Here we propose geometric mean of pairwise ratios-a simple but effective normalization method-for zero-inflated sequencing data such as microbiome data. Simulation studies and real datasets analyses demonstrate that the proposed method is more robust than competing methods, leading to more powerful detection of differentially abundant taxa and higher reproducibility of the relative abundances of taxa.

SUBMITTER: Chen L

PROVIDER: S-EPMC5885979 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data.

Chen Li L Reeve James J Zhang Lujun L Huang Shengbing S Wang Xuefeng X Chen Jun J

PeerJ 20180402

Normalization is the first critical step in microbiome sequencing data analysis used to account for variable library sizes. Current RNA-Seq based normalization methods that have been adapted for microbiome data fail to consider the unique characteristics of microbiome data, which contain a vast number of zeros due to the physical absence or under-sampling of the microbes. Normalization methods that specifically address the zero-inflation remain largely undeveloped. Here we propose geometric mean ...[more]

PMID: 29629248

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Project description:Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses. Analysis of microbial count data also requires special attention because data commonly exhibit zero inflation, i.e., more zeros than expected from a standard count distribution. To meet these needs, we developed a Bayesian variable selection model for multivariate count data with excess zeros that incorporates information on the covariance structure of the outcomes (counts for multiple taxa), while estimating associations with the mean levels of these outcomes. Though there has been much work on zero-inflated models for longitudinal data, little attention has been given to high-dimensional multivariate zero-inflated data modeled via a general correlation structure. Through simulation, we compared performance of the proposed method to that of existing univariate approaches, for both the binary ("excess zero") and count parts of the model. When outcomes were correlated the proposed variable selection method maintained type I error while boosting the ability to identify true associations in the binary component of the model. For the count part of the model, in some scenarios the univariate method had higher power than the multivariate approach. This higher power was at a cost of a highly inflated false discovery rate not observed with the proposed multivariate method. We applied the approach to oral microbiome data from the Pediatric HIV/AIDS Cohort Oral Health Study and identified five (of 44) species associated with HIV infection.

Project description:IntroductionCount outcomes in tobacco research are often analyzed with the Poisson distribution. However, they often exhibit features such as overdispersion (variance larger than expected) and zero inflation (extra zeros) that violate model assumptions. Furthermore, longitudinal studies have repeated measures that generate correlated counts. Failure to account for overdispersion, zero inflation, and correlation can yield incorrect statistical inferences. Thus, it is important to familiarize researchers with proper models for such data.Aims and methodsPoisson and Negative Binomial models with correlated random effects with and without zero inflation are presented. The illustrative data comes from a study comparing a mindfulness training app (Craving to Quit [C2Q], n = 60) with a control app (experience sampling-only app, n = 66) on smoking frequency at 1, 3, and 6 months. Predictors include app, time, the app-by-time interaction, and baseline smoking. Each model is evaluated in terms of accounting for zero inflation, overdispersion, and correlation in the data. Emphasis is placed on evaluating model fit, subject-specific interpretation of effects, and choosing an appropriate model.ResultsThe hurdle Poisson model provided the best fit to the data. Smoking abstinence rates were 33%, 32%, and 28% at 1, 3, and 6 months, respectively, with variance larger than expected by a factor >7 at each follow-up. Individuals on C2Q were less likely to achieve abstinence across time but likely to smoke fewer cigarettes if smoking.ConclusionsThe models presented are specifically suited for analyzing correlated count outcomes and account for zero inflation and overdispersion. We provide guidance to researchers on the use of these models to better inform nicotine and tobacco research.ImplicationsIn tobacco research, count outcomes are often measured repeatedly on the same subject and thus correlated. Such outcomes often have many zeros and exhibit large variances relative to the mean. Analyzing such data require models specifically suited for correlated counts. The presented models and guidelines could improve the rigor of the analysis of correlated count data and thus increase the impact of studies in nicotine and tobacco research using such outcomes.

Dataset Information

GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data.

Publications

GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data.

Similar Datasets

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