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Reliable Biomarker discovery from Metagenomic data via RegLRSD algorithm.

Mustafa Alshawaqfeh1, Ahmad Bashaireh1, Erchin Serpedin2

  • 1Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA.

BMC Bioinformatics
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PubMed
Summary

A new algorithm, Regularized Low Rank-Sparse Decomposition (RegLRSD), enhances biomarker detection in metagenomics. It ensures consistent results despite sample variations, improving clinical applications.

Keywords:
Alternating direction method of multipliersAugmented LagrangianBiomarker detectionMatrix decompositionMetagenomics

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Area of Science:

  • Metagenomics
  • Biomarker Discovery
  • Computational Biology

Background:

  • Biomarker detection translates biological data for clinical use.
  • Metagenomics suggests microbial dysbiosis as a disease biomarker.
  • Reproducibility is vital due to sample variability affecting biomarker algorithms.

Purpose of the Study:

  • Develop a robust biomarker detection algorithm.
  • Ensure consistent results irrespective of natural sample diversity.
  • Improve the reliability of metagenomic biomarker identification.

Main Methods:

  • Proposed the Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm.
  • Modeled bacterial abundance data using matrix decomposition (sparse and low-rank matrices).
  • Incorporated prior knowledge to enhance biological conclusion consistency.

Main Results:

  • RegLRSD models microbial abundance as sparse (differentially abundant) and low-rank (non-differentially abundant) components.
  • Demonstrated superior reproducibility and classification accuracy compared to state-of-the-art methods.
  • Identified a high-accuracy marker list across three realistic datasets.

Conclusions:

  • RegLRSD offers high reproducibility and classification accuracy for biomarker detection.
  • Performance is consistent across varying dataset complexities and biomarker numbers.
  • RegLRSD is a reliable tool for identifying potential metagenomic biomarkers.