Bayesian inference for biomarker discovery in proteomics: an analytic solution.

Noura Dridi, Audrey Giremus, Jean-Francois Giovannelli, Caroline Truntzer, Melita Hadzagic, Jean-Philippe Charrier, Laurent Gerfault, Patrick Ducoroy, Bruno Lacroix, Pierre Grangeat, Pascal Roy
{"title":"Bayesian inference for biomarker discovery in proteomics: an analytic solution.","authors":"Noura Dridi, Audrey Giremus, Jean-Francois Giovannelli, Caroline Truntzer, Melita Hadzagic, Jean-Philippe Charrier, Laurent Gerfault, Patrick Ducoroy, Bruno Lacroix, Pierre Grangeat, Pascal Roy","doi":"10.1186/s13637-017-0062-4","DOIUrl":null,"url":null,"abstract":"<p><p>This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought. In this way, correlations between the proteins are intrinsically taken into account in the decision. The developed strategy is derived in a Bayesian setting, and the decision is optimal in the sense that it minimizes a global mean error. It is finally based on the posterior probabilities of the partitions. The main difficulty is to calculate these probabilities since they are based on the so-called evidence that require marginalization of all the unknown model parameters. Two models are presented that relate the status to the protein concentrations, depending whether the latter are biomarkers or not. The first model accounts for biological variabilities by assuming that the concentrations are Gaussian distributed with a mean and a covariance matrix that depend on the status only for the biomarkers. The second one is an extension that also takes into account the technical variabilities that may significantly impact the observed concentrations. The main contributions of the paper are: (1) a new Bayesian formulation of the biomarker selection problem, (2) the closed-form expression of the posterior probabilities in the noiseless case, and (3) a suitable approximated solution in the noisy case. The methods are numerically assessed and compared to the state-of-the-art methods (t test, LASSO, Battacharyya distance, FOHSIC) on synthetic and real data from proteins quantified in human serum by mass spectrometry in selected reaction monitoring mode.</p>","PeriodicalId":72957,"journal":{"name":"EURASIP journal on bioinformatics & systems biology","volume":"2017 1","pages":"9"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13637-017-0062-4","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURASIP journal on bioinformatics & systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s13637-017-0062-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/7/14 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought. In this way, correlations between the proteins are intrinsically taken into account in the decision. The developed strategy is derived in a Bayesian setting, and the decision is optimal in the sense that it minimizes a global mean error. It is finally based on the posterior probabilities of the partitions. The main difficulty is to calculate these probabilities since they are based on the so-called evidence that require marginalization of all the unknown model parameters. Two models are presented that relate the status to the protein concentrations, depending whether the latter are biomarkers or not. The first model accounts for biological variabilities by assuming that the concentrations are Gaussian distributed with a mean and a covariance matrix that depend on the status only for the biomarkers. The second one is an extension that also takes into account the technical variabilities that may significantly impact the observed concentrations. The main contributions of the paper are: (1) a new Bayesian formulation of the biomarker selection problem, (2) the closed-form expression of the posterior probabilities in the noiseless case, and (3) a suitable approximated solution in the noisy case. The methods are numerically assessed and compared to the state-of-the-art methods (t test, LASSO, Battacharyya distance, FOHSIC) on synthetic and real data from proteins quantified in human serum by mass spectrometry in selected reaction monitoring mode.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
蛋白质组学中生物标志物发现的贝叶斯推断:分析解决方案。
本文讨论了蛋白质组学中生物标志物的发现问题。给定关于一组个体的蛋白质列表的临床数据,解决的问题是提取一小部分蛋白质,其浓度是生物状态(健康或病理)的指标。本文将其表述为变量选择的具体实例。该方法的独创性在于不逐个研究蛋白质,而是直接寻求区分蛋白质和非区分蛋白质之间的最佳划分。通过这种方式,在决策中本质上考虑了蛋白质之间的相关性。所开发的策略是在贝叶斯环境中导出的,并且决策是最优的,因为它使全局平均误差最小化。最后,它是基于分区的后验概率。主要的困难是计算这些概率,因为它们是基于所谓的证据,需要将所有未知的模型参数边缘化。提出了两种模型,将状态与蛋白质浓度联系起来,取决于后者是否为生物标志物。第一个模型通过假设浓度是高斯分布的,其平均值和协方差矩阵仅取决于生物标记物的状态,来解释生物变异性。第二个是一个扩展,它也考虑了可能对观测到的浓度产生重大影响的技术变化。本文的主要贡献有:(1)生物标志物选择问题的新贝叶斯公式,(2)无噪声情况下后验概率的封闭形式表达式,以及(3)有噪声情况下的合适近似解。在选定的反应监测模式下,对这些方法进行数值评估,并与最先进的方法(t检验、LASSO、Battacharyya距离、FOHSIC)进行比较,使用质谱法对人血清中蛋白质的合成和真实数据进行定量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data. On biometric systems: electrocardiogram Gaussianity and data synthesis. BCC-NER: bidirectional, contextual clues named entity tagger for gene/protein mention recognition. Review of stochastic hybrid systems with applications in biological systems modeling and analysis. Bayesian inference for biomarker discovery in proteomics: an analytic solution.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1