ArticleBioinformatics (Oxford, England)2024
Zero is not absence: censoring-based differential abundance analysis for microbiome data.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A joint mixture Tobit method with latent microbial abundance improves detection of microbiome-disease associations in zero-inflated data.PLoS computational biology · 2026Article
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Linking composition and function: a two-year metatranscriptomic study of microbial adaptation in an industrial wastewater treatment plant.FEMS microbiology ecology · 2026Article
- Medical statistics education in the big data era: focusing on critical thinking and ethical competence.Frontiers in public health · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- A realistic simulation-based benchmark of microbiome normalization in sample stratification and taxa-level analysis.Frontiers in bioinformatics · 2026Article
- CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells.bioRxiv : the preprint server for biology · 2025Article
- Composition-on-composition regression analysis for multi-omics integration of metagenomic data.Bioinformatics (Oxford, England) · 2025Article
- ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models.Bioinformatics (Oxford, England) · 2024Article
- ADAPT: Analysis of Microbiome Differential Abundance by Pooling Tobit Models.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationMicrobiome data analysis faces the challenge of sparsity, with many entries recorded as zeros. In differential abundance analysis, the presence of excessive zeros in data violates distributional assumptions and creates ties, leading to an increased risk of type I errors and reduced statistical power.
resultsWe developed a novel normalization method, called censoring-based analysis of microbiome proportions (CAMP), for microbiome data by treating zeros as censored observations, transforming raw read counts into tie-free time-to-event-like data. This enables the use of survival analysis techniques, like the Cox proportional hazards model, for differential abundance analysis. Extensive simulations demonstrate that CAMP achieves proper type I error control and high power. Applying CAMP to a human gut microbiome dataset, we identify 60 new differentially abundant taxa across geographic locations, showcasing its usefulness. CAMP overcomes sparsity challenges, enabling improved statistical analysis and providing valuable insights into microbiome data in various contexts. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/lapsumchan/CAMP.
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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.