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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

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PreLect: Prevalence leveraged consistent feature selection decodes microbial signatures across cohorts.

Yin-Cheng Chen¹, Yin-Yuan Su¹, Tzu-Yu Chu¹

¹Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.

NPJ Biofilms and Microbiomes

|January 3, 2025

Summary

PreLect, a new feature selection framework, improves the identification of key microbes in complex microbiota data. It enhances classification accuracy and finds reproducible microbial signatures for research and clinical use.

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Area of Science:

Microbiome research
Bioinformatics
Computational biology

Background:

Microbiota sequencing data is high-dimensional and sparse, challenging the identification of informative and reproducible microbial features.
Existing feature selection methods struggle with the complexity of microbiota data, impacting research and clinical applications.

Purpose of the Study:

To introduce PreLect, an innovative feature selection framework designed to address the challenges of sparse microbiota data.
To enhance the identification of informative and reproducible microbial features for research and clinical applications.

Main Methods:

PreLect utilizes microbial prevalence for consistent feature selection in sparse datasets.
The framework was rigorously benchmarked against established statistical and machine learning-based feature selection methodologies.
Performance was evaluated across 42 diverse microbiome datasets.

Main Results:

PreLect demonstrated superior classification capabilities compared to traditional statistical methods.
It outperformed machine learning-based methods by selecting features with greater prevalence and abundance.
PreLect reliably identified reproducible microbial features across varied cohorts.

Conclusions:

PreLect offers an accurate and robust advancement for analyzing complex microbiota data.
The framework effectively identifies key microbes and crucial biological pathways, such as those in colorectal cancer progression.
PreLect's ability to discern clinically relevant microbial signatures holds significant potential for future research and applications.