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LinDA: linear models for differential abundance analysis of microbiome compositional data
Huijuan Zhou1,2,3, Kejun He3, Jun Chen4
1Shanghai University of Finance and Economics, Shanghai, 200437, China.
Genome Biology
|April 15, 2022
Summary
We introduce LinDA, a novel method for microbiome differential abundance analysis. LinDA effectively controls false positives in compositional data using linear regression, offering a scalable and flexible solution.
Area of Science:
- Microbiome research
- Statistical analysis
- Bioinformatics
Background:
- Differential abundance analysis is crucial for microbiome studies.
- Microbiome sequencing data is compositional, posing challenges for false positive control.
- Existing methods may struggle with the inherent biases of compositional data.
Purpose of the Study:
- To develop a robust method for differential abundance analysis in microbiome data.
- To address the challenges posed by the compositional nature of sequencing data.
- To provide a flexible and scalable approach for microbiome data analysis.
Main Methods:
- Proposed a new method named LinDA (Linear models for Differential Abundance).
- Utilized centered log-ratio (CLR) transformation of microbiome data.
- Applied linear regression models and corrected for compositional bias.
Main Results:
- LinDA demonstrates asymptotic False Discovery Rate (FDR) control.
- The method is scalable and flexible, applicable to complex study designs.
- LinDA effectively handles correlated microbiome data through extensions to mixed-effect models.
Conclusions:
- LinDA offers a statistically sound and practical approach to microbiome differential abundance analysis.
- The method successfully addresses compositional effects, improving false positive control.
- LinDA's effectiveness is validated through simulations and real-world microbiome datasets.
