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CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells.

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  • 1Department of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA, 30322, United States.

Biorxiv : the Preprint Server for Biology
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

The new Compositional Accelerated Failure Time (CAFT) model offers robust differential abundance analysis for microbiome data, outperforming existing methods in controlling errors and identifying microbial differences in IBD and respiratory tract studies.

Keywords:
CAFTFDR controlMicrobiomebiascompositionalitydifferentially abundantinflated zerossensitivity

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • Microbiome data analysis is crucial for understanding host-microbe interactions but faces challenges due to data compositionality, sparsity, and experimental biases.
  • Standard statistical methods often fail to adequately address these unique features, potentially leading to inaccurate findings and poor false discovery rate (FDR) control.
  • Existing approaches may overlook data characteristics or use pseudocounts, compromising the reliability of differential abundance analyses.

Purpose of the Study:

  • To introduce a novel framework, the Compositional Accelerated Failure Time (CAFT) model, for robust differential abundance analysis of microbiome data.
  • To address limitations of current methods by effectively handling zero counts, compositional bias, and technical biases.
  • To provide a more accurate and reliable tool for identifying microbial differences in complex biological samples.

Main Methods:

  • The Compositional Accelerated Failure Time (CAFT) model treats zero read counts as censored data below a detection limit.
  • This approach inherently resists multiplicative technical bias and eliminates the need for pseudocounts.
  • CAFT employs score test procedures to effectively manage compositional bias in microbiome datasets.

Main Results:

  • Extensive simulations demonstrate that CAFT surpasses existing compositional differential abundance methods in type I error and FDR control, even in the presence of technical bias.
  • CAFT showed superior performance compared to LOCOM, LinDA, ANCOM-BC2, its robust variant, and LDM-clr.
  • Application to inflammatory bowel disease (IBD) and upper respiratory tract (URT) data successfully identified differentially abundant taxa, distinguishing IBD patients from controls and smokers from non-smokers.

Conclusions:

  • The Compositional Accelerated Failure Time (CAFT) model is presented as a powerful, robust, and efficient tool for analyzing compositional microbiome data.
  • CAFT demonstrates superior control of Type I error and maintains FDR control, with enhanced statistical testing power.
  • This makes CAFT a valuable advancement for microbiome research, offering improved accuracy and reliability in differential abundance analyses.