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Evaluating supervised and unsupervised background noise correction in human gut microbiome data.

Leah Briscoe¹, Brunilda Balliu², Sriram Sankararaman^3,4,2

¹Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America.

Plos Computational Biology

|February 7, 2022

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This summary is machine-generated.

Identifying biomarkers from microbiome data is challenging due to technical noise. An unsupervised principal component correction method shows promise for reducing false biomarker discoveries without prior knowledge of variation sources.

Area of Science:

Microbiome research
Computational biology
Biostatistics

Background:

Predicting human phenotypes and identifying disease biomarkers from metagenomic data is vital for developing microbiome-associated therapeutics.
Technical variables (e.g., sequencing protocols) in metagenomic data introduce noise, complicating phenotype prediction and biomarker discovery.
Existing supervised noise correction methods, adapted from gene expression data, may fail to account for unmeasured variation sources in microbiome data.

Purpose of the Study:

To compare the effectiveness of different denoising transformations and correction methods for metagenomic data.
To evaluate an unsupervised principal component correction approach for microbiome data analysis.
To assess the impact of noise correction on phenotype prediction and biomarker discovery.

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Main Methods:

Comparative analysis of supervised denoising transformations combined with correction methods.
Application and evaluation of an unsupervised principal component correction approach on microbiome data.
Assessment of biomarker discovery and phenotype prediction accuracy before and after noise correction.

Main Results:

The unsupervised principal component correction approach demonstrated comparable efficacy to supervised methods in reducing false biomarker discoveries.
This unsupervised method offers the advantage of not requiring prior knowledge of variation sources.
In prediction tasks, the unsupervised approach improved performance only when technical variables constituted the majority of the data variance.

Conclusions:

Unsupervised principal component correction is a viable strategy for reducing false discoveries in microbiome biomarker analysis.
This method enhances reproducibility in microbiome analyses, especially with increasing dataset sizes.
Further research is needed to optimize unsupervised approaches for diverse microbiome data characteristics and prediction tasks.