MixTwice: large-scale hypothesis testing for peptide arrays by variance mixing.
MixTwice: large-scale hypothesis testing for peptide arrays by variance mixing.
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DOI：
10.1093/bioinformatics/btab162
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发表时间：
2021-09-09
期刊：
影响因子：
--
通讯作者：
Newton MA
中科院分区：
文献类型：
--
作者：
Zheng Z;Mergaert AM;Ong IM;Shelef MA;Newton MA
肽微阵列已经成为免疫蛋白质组学中的一项强大技术，因为它们提供了一种工具来测量患者血清样本中不同抗体的丰度。许多实验的高维度和小样本量挑战了传统的统计方法，包括那些旨在控制错误发现率（FDR）的方法。由于现有方法的可重复性和有效性的局限性，我们提出了一种经验贝叶斯工具，当提供所有测量肽的估计效应和估计标准误差的数据时，该工具可以计算局部FDR统计数据和局部假信号率统计数据。顾名思义，MixTwice工具涉及对两个混合分布的估计，一个是对潜在影响的估计，另一个是对潜在方差参数的估计。约束优化技术提供了弱形状约束下（效应分布单峰）混合分布的模型拟合。数值实验表明，MixTwice算法能够准确估计生成参数，有效识别非无效肽。在类风湿性关节炎的肽阵列研究中，MixTwice在信号较弱的情况下恢复了有意义的肽标记，并且在信号较强的情况下具有很强的再现性。MixTwice是一个R软件包https://cran.r-project.org/web/packages/MixTwice/。补充数据可在生物信息学网站获得。
Peptide microarrays have emerged as a powerful technology in immunoproteomics as they provide a tool to measure the abundance of different antibodies in patient serum samples. The high dimensionality and small sample size of many experiments challenge conventional statistical approaches, including those aiming to control the false discovery rate (FDR). Motivated by limitations in reproducibility and power of current methods, we advance an empirical Bayesian tool that computes local FDR statistics and local false sign rate statistics when provided with data on estimated effects and estimated standard errors from all the measured peptides. As the name suggests, the MixTwice tool involves the estimation of two mixing distributions, one on underlying effects and one on underlying variance parameters. Constrained optimization techniques provide for model fitting of mixing distributions under weak shape constraints (unimodality of the effect distribution). Numerical experiments show that MixTwice can accurately estimate generative parameters and powerfully identify non-null peptides. In a peptide array study of rheumatoid arthritis, MixTwice recovers meaningful peptide markers in one case where the signal is weak, and has strong reproducibility properties in one case where the signal is strong. MixTwice is available as an R software package https://cran.r-project.org/web/packages/MixTwice/. Supplementary data are available at Bioinformatics online.
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影响因子：
13.3
作者：
Sokolove, Jeremy;Johnson, Dannette S.;Lahey, Lauren J.;Wagner, Catriona A.;Cheng, Danye;Thiele, Geoffrey M.;Michaud, Kaleb;Sayles, Harlan;Reimold, Andreas M.;Caplan, Liron;Cannon, Grant W.;Kerr, Gail;Mikuls, Ted R.;Robinson, William H.
通讯作者：
Robinson, William H.
影响因子：
5.6
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Szarka, Eszter;Aradi, Petra;Sarmay, Gabriella
通讯作者：
Sarmay, Gabriella
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11.2
作者：
Yan, Yuanqing;Sun, Nan;Hanash, Samir M.
通讯作者：
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模因套件：用于发现和搜索的工具。
影响因子：
14.9
作者：
Bailey TL;Boden M;Buske FA;Frith M;Grant CE;Clementi L;Ren J;Li WW;Noble WS
通讯作者：
Noble WS
影响因子：
6.4
作者：
Mishra N;Caciula A;Price A;Thakkar R;Ng J;Chauhan LV;Jain K;Che X;Espinosa DA;Montoya Cruz M;Balmaseda A;Sullivan EH;Patel JJ;Jarman RG;Rakeman JL;Egan CT;Reusken CBEM;Koopmans MPG;Harris E;Tokarz R;Briese T;Lipkin WI
通讯作者：
Lipkin WI