Reliable Biomarker discovery from Metagenomic data via RegLRSD algorithm

BMC Bioinformatics

Research Article

Erchin Serpedin¹  Mustafa Alshawaqfeh¹  Ahmad Bashaireh¹  Jan Suchodolski² 

[1] Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, 77843-3128, College Station, TX, USA;College of Veterinary Medicine and Biomedical Sciences, Gastrointestinal Laboratory, Texas A&M University, 77843-3128, College Station, TX, USA;

关键词: Biomarker detection;    Metagenomics;    Matrix decomposition;    Alternating direction method of multipliers;    Augmented Lagrangian;   

DOI  :  10.1186/s12859-017-1738-1

 received in 2017-04-24, accepted in 2017-06-22,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundBiomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed.ResultsToward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy.ConclusionsThe proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.

【 授权许可】

CC BY   
© The Author(s) 2017

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