Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
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DOI：
10.1371/journal.pone.0119254
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Schafer DW
中科院分区：
文献类型：
--
作者：
Mi G;Di Y;Schafer DW
本文主要研究负二项（NB）回归模型的适当性评估，特别是（1）评估NB假设的适当性，（2）评估NB离差参数模型的适当性。第一种工具通常适用于NB回归;第二种工具主要用于RNA测序（RNA-Seq）数据分析。RNA-Seq分析中通常少量的生物样品和大量的基因促使我们使用NB回归模型来解决鲁棒性和统计功效之间的权衡。例如，一种广泛使用的节能策略是通过将NB分散参数与平均表达率相关联的简单模型来假设基因之间的NB分散参数的一些共性，并且已经提出了许多这样的模型。随着RNA-Seq分析变得越来越流行，对所产生的方法的能力和鲁棒性以及模型评估的实用工具进行更深入的研究是合适的。在这篇文章中，我们提出了基于模拟的统计测试和诊断图形，以解决模型的充分性。我们提供了模拟和真实的数据的例子来说明，我们提出的方法是有效的检测NB均值方差关系的误设定，以及判断几个NB色散模型的拟合充分。
This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
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影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
3
作者：
Esnaola M;Puig P;Gonzalez D;Castelo R;Gonzalez JR
通讯作者：
Gonzalez JR
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
DOI：
10.1515/1544-6115.1826
发表时间：
2012-01-01
影响因子：
0.9
作者：
Lund, Steven P.;Nettleton, Dan;Smyth, Gordon K.
通讯作者：
Smyth, Gordon K.
影响因子：
0.8
作者：
Svetliza, CF;Paula, GA
通讯作者：
Paula, GA