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Robust Differential Abundance Analysis of Microbiome Sequencing Data
Guanxun Li1, Lu Yang2, Jun Chen2
1Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
Genes
|November 25, 2023
Summary
Microbiome data often contain outliers and heavy tails, which can reduce the power of differential abundance analysis (DAA). Robust Huber regression effectively mitigates these issues, improving DAA results.
Area of Science:
- Microbiology
- Statistical Bioinformatics
Background:
- Microbiome data frequently exhibit outliers and heavy-tailed distributions.
- The impact of these data characteristics on differential abundance analysis (DAA) remains underexplored.
Purpose of the Study:
- To systematically investigate the influence of outliers and heavy-tailedness on DAA using the Linear Models for Differential Abundance analysis (LinDA) method.
- To propose and evaluate strategies for mitigating these data challenges.
Main Methods:
- Examined the impact of outliers and heavy-tailedness on LinDA.
- Investigated data winsorization as a preprocessing step.
- Generalized LinDA to incorporate robust regression techniques, specifically Huber regression.
Main Results:
- Outliers and heavy-tailedness significantly reduce the statistical power of standard LinDA.
- Winsorizing data showed some improvement.
- Robust Huber regression within a generalized LinDA framework demonstrated superior performance in addressing outliers and heavy-tailedness.
Conclusions:
- Standard DAA methods like LinDA are sensitive to outliers and heavy-tailed microbiome data.
- Robust Huber regression offers an effective solution for improving the reliability of DAA in the presence of such data characteristics.
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