ArticleFrontiers in genetics2020
A Zero-Inflated Latent Dirichlet Allocation Model for Microbiome Studies.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Microbes Under Climate Refugia: Equable Subcommunity Rank Dynamics in Large-River Deltaic Estuaries.Ecology and evolution · 2025Article
- ISCAZIM: Integrated statistical correlation analysis for zero-inflated microbiome data.Heliyon · 2025Article
- Cell-to-cell distance that combines gene expression and gene embeddings.Computational and structural biotechnology journal · 2024Article
- Development of shoulder osteoarthritis and bone lesions in female and male rats subjected to a high fat/sucrose diet.Scientific reports · 2024Article
- Randomized feature selection based semi-supervised latent Dirichlet allocation for microbiome analysis.Scientific reports · 2024Article
- Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation.Biometrics · 2023Article
- Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism.Scientific reports · 2023Article
- Oral microbiome and ischemic stroke risk among elderly Chinese women.Journal of oral microbiology · 2023Article
- Risk of bias assessment tool for systematic review and meta-analysis of the gut microbiome.Gut microbiome (Cambridge, England) · 2023Article
- Investigating differential abundance methods in microbiome data: A benchmark study.PLoS computational biology · 2022Review
- Negative binomial factor regression with application to microbiome data analysis.Statistics in medicine · 2022Article
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2 authors.
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Abstract
The human microbiome consists of a community of microbes in varying abundances and is shown to be associated with many diseases. An important first step in many microbiome studies is to identify possible distinct microbial communities in a given data set and to identify the important bacterial taxa that characterize these communities. The data from typical microbiome studies are high dimensional count data with excessive zeros due to both absence of species (structural zeros) and low sequencing depth or dropout. Although methods have been developed for identifying the microbial communities based on mixture models of counts, these methods do not account for excessive zeros observed in the data and do not differentiate structural from sampling zeros. In this paper, we introduce a zero-inflated Latent Dirichlet Allocation model (zinLDA) for sparse count data observed in microbiome studies. zinLDA builds on the flexible Latent Dirichlet Allocation model and allows for zero inflation in observed counts. We develop an efficient Markov chain Monte Carlo (MCMC) sampling procedure to fit the model. Results from our simulations show zinLDA provides better fits to the data and is able to separate structural zeros from sampling zeros. We apply zinLDA to the data set from the American Gut Project and identify microbial communities characterized by different bacterial genera.
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