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Brief Bioinform. 2019 Jun 12. pii: bbz061. doi: 10.1093/bib/bbz061. [Epub ahead of print]

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies.

Author information

1
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
2
Department of Bioinformatics, Chongqing Medical University, Chongqing, China.
3
School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.
4
Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.

Abstract

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

KEYWORDS:

classification accuracy; feature selection method; metaproteomics; microbiome; spiked proteins

PMID:
31197323
DOI:
10.1093/bib/bbz061

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