Zero is not absence: censoring-based differential abundance analysis for microbiome data.

ABSTRACT:

Motivation

Microbiome 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.

Results

We 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 p

SUBMITTER: Chan LS 

PROVIDER: S-EPMC10885211 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature