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Updated: Feb 8, 2026

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

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Variable selection in omics data: A practical evaluation of small sample sizes.

Alexander Kirpich^1,2, Elizabeth A Ainsworth^3,4, Jessica M Wedow³

¹Department of Biology, University of Florida, Gainesville, FL, United States of America.

|June 22, 2018

Summary

This summary is machine-generated.

For omics studies, ANOVA is effective for initial biomarker screening, showing low false positive rates. It outperforms Elastic Net in reducing features for further testing while limiting errors.

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Area of Science:

Genomics
Metabolomics
Bioinformatics

Background:

Omics experiments face the n < p problem with many variables and few samples.
Correlated variables and experimental design structures complicate biomarker identification.
Overfitting is a common issue with existing biomarker identification methods.

Purpose of the Study:

To reframe omics studies as screening studies to reduce features for subsequent testing and limit Type II errors.
To compare the performance of LASSO, ridge regression, and Elastic Net against ANOVA for feature selection in omics data.
To evaluate analytical tools for initial screening of features in omics experiments.

Main Methods:

A simulation study was conducted to compare statistical methods.
Two real omics datasets were used for comparative analysis.
Performance was evaluated based on Type I and Type II error rates.

Main Results:

ANOVA demonstrated a low Type I error rate, unaffected by a large number of features.
Elastic Net exhibited an inflated Type I error rate, increasing with sample size.
ANOVA showed comparable or lower Type II error rates than Elastic Net.

Conclusions:

ANOVA is an effective tool for the initial screening of features in omics experiments.
ANOVA's robustness against a high number of features and low error rates make it suitable for biomarker discovery.
The study advocates for viewing omics experiments as screening studies to improve biomarker identification accuracy.