Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates
复制标题
DOI：
10.1515/1544-6115.1826
复制
发表时间：
2012-01-01
影响因子：
0.9
通讯作者：
Smyth, Gordon K.
中科院分区：
文献类型：
--
作者：
Lund, Steven P.;Nettleton, Dan;Smyth, Gordon K.
下一代测序技术为以RNA序列数据的形式测量基因表达水平提供了强大的工具。从RNA-SEQ数据中识别差异表达(DE)基因的方法开发是当前研究的一个热门领域。RNA-SEQ数据通常包括许多低计数的整数，并且相对于泊松或二项分布可能表现出严重的过度离散性。在这里，我们提出了基于Smyth(2004)估计微阵列数据的基因特定误差方差的改编方法的具有缩小离散度估计的准似然方法。我们建议的方法计算简单，类似于ANOVA，并且在基于真实数据的各种模拟中检测DE基因和估计错误发现率方面比竞争方法更有利。
Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth's (2004) approach to estimating gene-specific error variances for microarray data. Our suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.