A joint mixture Tobit method with latent microbial abundance improves detection of microbiome-disease associations in zero-inflated data.

A joint mixture Tobit method with latent microbial abundance enhances detection of disease-associated microbes in zero-inflated microbiome data, improving power and stability.

• Why it matters: Zero inflation complicates microbiome analysis, leading to false positives or missed signals, and existing methods often fail to distinguish between different zero-generating mechanisms, limiting reliable inference.
• What they did: The authors developed a joint mixture Tobit model incorporating a point-mass for structural zeros and a Tobit regression for latent abundance, linking disease status, covariates, and microbial counts in a unified framework.
• The result: The method effectively controls type I error, maintains high power, and identifies meaningful taxa in colorectal cancer datasets, advancing microbiome-disease association studies.