4.6 Article

PBLMM: Peptide-based linear mixed models for differential expression analysis of shotgun proteomics data

期刊

JOURNAL OF CELLULAR BIOCHEMISTRY
卷 123, 期 3, 页码 691-696

出版社

WILEY
DOI: 10.1002/jcb.30225

关键词

bioinformatics; data analysis; differential expression; proteomics; statistics

资金

  1. Deutsche Forschungsgemeinschaft [259130777-SFB1177, 390339347, 403765277]
  2. Seventh Framework Programme [ERC StG 803565]

向作者/读者索取更多资源

We introduce a peptide-based linear mixed models tool, PBLMM, as a standalone desktop application for differential expression analysis of proteomics data. We also offer a Python package that enables streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM, which uses peptide-based linear mixed regression models, is user-friendly and does not require scripting experience. Our study demonstrates that peptide-based models outperform classical methods in statistically inferring differentially expressed proteins. Furthermore, PBLMM exhibits superior statistical power, especially in situations with low effect size and/or low sample size.
Here, we present a peptide-based linear mixed models tool-PBLMM, a standalone desktop application for differential expression analysis of proteomics data. We also provide a Python package that allows streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM is easy to use without scripting experience and calculates differential expression by peptide-based linear mixed regression models. We show that peptide-based models outperform classical methods of statistical inference of differentially expressed proteins. In addition, PBLMM exhibits superior statistical power in situations of low effect size and/or low sample size. Taken together our tool provides an easy-to-use, high-statistical-power method to infer differentially expressed proteins from proteomics data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据