Dataset Information

MbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.

ABSTRACT: The analysis of microbiome data has several technical challenges. In particular, count matrices contain a large proportion of zeros, some of which are biological, whereas others are technical. Furthermore, the measurements suffer from unequal sequencing depth, overdispersion, and data redundancy. These nuisance factors introduce substantial noise. We propose an accurate and robust method, mbDenoise, for denoising microbiome data. Assuming a zero-inflated probabilistic PCA (ZIPPCA) model, mbDenoise uses variational approximation to learn the latent structure and recovers the true abundance levels using the posterior, borrowing information across samples and taxa. mbDenoise outperforms state-of-the-art methods to extract the signal for downstream analyses.

SUBMITTER: Zeng Y

PROVIDER: S-EPMC9011970 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.

Zeng Yanyan Y Li Jing J Wei Chaochun C Zhao Hongyu H Wang Tao T

Genome biology 20220414 1

The analysis of microbiome data has several technical challenges. In particular, count matrices contain a large proportion of zeros, some of which are biological, whereas others are technical. Furthermore, the measurements suffer from unequal sequencing depth, overdispersion, and data redundancy. These nuisance factors introduce substantial noise. We propose an accurate and robust method, mbDenoise, for denoising microbiome data. Assuming a zero-inflated probabilistic PCA (ZIPPCA) model, mbDenoi ...[more]

PMID: 35422001

Similar Datasets

Project description:BackgroundIdentifying bacterial taxa associated with disease phenotypes or clinical treatments over time is critical for understanding the underlying biological mechanism. Association testing for microbiome data is already challenging due to its complex distribution that involves sparsity, over-dispersion, heavy tails, etc. The longitudinal nature of the data adds another layer of complexity - one needs to account for the within-subject correlations to avoid biased results. Existing longitudinal differential abundance approaches usually depend on strong parametric assumptions, such as zero-inflated normal or negative binomial. However, the complex microbiome data frequently violate these distributional assumptions, leading to inflated false discovery rates. In addition, the existing methods are mostly mean-based, unable to identify heterogeneous associations such as tail events or subgroup effects, which could be important biomedical signals.MethodsWe propose a zero-inflated quantile approach for longitudinal (ZINQ-L) microbiome differential abundance test. A mixed-effects quantile rank-score-based test was proposed for hypothesis testing, which consists of a test in mixed-effects logistic model for the presence-absence status of the investigated taxon, and a series of mixed-effects quantile rank-score tests adjusted for zero inflation given its presence. As a regression method with minimal distributional assumptions, it is robust to the complex microbiome data, controlling false discovery rate, and is flexible to adjust for important covariates. Its comprehensive examination of the abundance distribution enables the identification of heterogeneous associations, improving the testing power.ResultsExtensive simulation studies and an application to a real kidney transplant microbiome study demonstrate the improved power of ZINQ-L in detecting true signals while controlling false discovery rates.ConclusionZINQ-L is a zero-inflated quantile-based approach for detecting individual taxa associated with outcomes or exposures in longitudinal microbiome studies, providing a robust and powerful option to improve and complement the existing methods in the field.

Dataset Information

MbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.

Publications

mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets