Generalized linear and mixed models for label-free shotgun proteomics

被引：0

作者：

Leitch, Matthew C. ^{[1
]}

Mitra, Indranil ^{[1
]}

Sadygov, Rovshan G. ^{[1
]}

机构：

[1] Univ Texas Med Branch, Sealy Ctr Mol Med, Dept Biochem & Mol Biol, Galveston, TX 77555 USA

来源：

STATISTICS AND ITS INTERFACE | 2012年 / 5卷 / 01期

关键词：

Count data; Statistical models; Spectral count; Label-free quantitative proteomics; p-values; FDR; Negative binomial model; Quasi-Poisson model; Mixture model; Generalized linear models; RELATIVE PROTEIN ABUNDANCE; SPECTRAL COUNT DATA; MASS-SPECTROMETRY; SACCHAROMYCES-CEREVISIAE; LIQUID-CHROMATOGRAPHY; CLUSTERING ANALYSIS; POISSON APPROACH; MIXTURES; IDENTIFICATION; QUANTITATION;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Label-free shotgun proteomics holds great promise, and has already had some great successes in pinpointing which proteins are up or down regulated in certain disease states. However, there are still some pressing issues concerning the statistical analysis of label-free shotgun proteomics, and this field has not enjoyed as much dedication of statistical research towards it as microarray research has. Here we reapply previously used statistical methods, the QSpec and quasi-Poisson, as well as apply the negative binomial distribution to both a control data set and a data set with known differential expression to determine the successes and failure of each of the three methods.

引用

页码：89 / 98

页数：10

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