Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.
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DOI：
10.1093/nar/gks042
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发表时间：
2012-05
影响因子：
14.9
通讯作者：
Smyth GK
中科院分区：
文献类型：
--
作者：
McCarthy DJ;Chen Y;Smyth GK
开发了一种灵活的统计框架，用于分析RNA - Seq基因表达研究中的读数计数。它能够分析涉及多种处理条件和区组变量的复杂实验，同时充分考虑生物变异。RNA样本之间的生物变异与测序技术相关的技术变异分开估计。新的经验贝叶斯方法允许每个基因具有其自身特定的变异性，即使只有相对较少的生物重复来估计这种变异性。该流程在Bioconductor项目的edgeR软件包中实现。对癌症数据的案例研究分析展示了广义线性模型方法（GLMs）在配对设计中检测差异表达的能力，甚至能够检测肿瘤特异性的表达变化。该案例研究表明需要考虑基因特异性变异，而不是假设基因间具有共同的离散度或丰度与变异性之间存在固定关系。按基因的离散度会降低结果不一致的基因的优先级，并使主要分析能够聚焦于生物重复之间一致的变化。开发了并行计算方法，以使非线性模型拟合更快、更可靠，从而使GLMs在基因组数据中的应用更加方便和实用。模拟实验展示了调整后的轮廓似然估计量在复杂情况下能够返回准确的生物变异性估计值。当变异是基因特异性时，经验贝叶斯估计量在假设共同离散度或单独的按基因离散度这两个极端之间提供了一种有利的折衷。这里开发的方法也可应用于DNA - Seq应用产生的计数数据，包括用于表观遗传标记的ChIP - Seq和DNA甲基化分析。
A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
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DOI：
10.1093/biostatistics/kxr054
发表时间：
2012-04
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Hansen KD;Irizarry RA;Wu Z
通讯作者：
Wu Z
影响因子：
7
作者：
Blekhman, Ran;Marioni, John C.;Gilad, Yoav
通讯作者：
Gilad, Yoav
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
影响因子：
12.3
作者：
Denoeud F;Aury JM;Da Silva C;Noel B;Rogier O;Delledonne M;Morgante M;Valle G;Wincker P;Scarpelli C;Jaillon O;Artiguenave F
通讯作者：
Artiguenave F
影响因子：
48
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
通讯作者：
Grimmond, Sean M.