MetPC: Metabolite Pipeline Consisting of Metabolite Identification and Biomarker Discovery Under the Control of Two-Dimensional FDR

被引:1
|
作者
Kim, Jaehwi [1 ]
Jeong, Jaesik [2 ]
机构
[1] Duksung Womens Univ, Stat Informat Dept, Seoul 01369, South Korea
[2] Chonnam Natl Univ, Stat Dept, Kwangju 61186, South Korea
来源
METABOLITES | 2019年 / 9卷 / 05期
基金
新加坡国家研究基金会;
关键词
hierarchical statistical model; fdr2d; metabolite identification; latent variable; Expectation-Maximization; ALIGNMENT; RATES;
D O I
10.3390/metabo9050103
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Due to the complex features of metabolomics data, the development of a unified platform, which covers preprocessing steps to data analysis, has been in high demand over the last few decades. Thus, we developed a new bioinformatics tool that includes a few of preprocessing steps and biomarker discovery procedure. For metabolite identification, we considered a hierarchical statistical model coupled with an Expectation-Maximization (EM) algorithm to take care of latent variables. For biomarker metabolite discovery, our procedure controls two-dimensional false discovery rate (fdr2d) when testing for multiple hypotheses simultaneously.
引用
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页数:11
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