Bayesian negative binomial regression for differential expression with confounding factors
Bayesian negative binomial regression for differential expression with confounding factors
复制标题
DOI：
10.1093/bioinformatics/bty330
复制
发表时间：
2018-10-01
期刊：
影响因子：
5.8
通讯作者：
Qian, Xiaoning
中科院分区：
文献类型：
--
作者：
Dadaneh, Siamak Zamani;Zhou, Mingyuan;Qian, Xiaoning
动机：高通量测序技术的快速采用使人们能够更好地理解与生物医学研究中的表型差异相关的全基因组分子图谱变化。通常，这些变化是由多个相互作用的因素造成的。现有的方法大多是考虑两个条件的差异表达，研究一个主要因素，而没有考虑其他混杂因素。此外，它们经常伴随着必要的复杂的特别前处理步骤，如归一化，限制了它们对一般实验设置的适应性。精确破译基因-表型关系的复杂多因素实验设计表明，需要开发有效的统计工具来分析多因素条件下的基因组测序数据。结果：我们开发了一种新的贝叶斯负二项回归(BNB-R)方法来分析RNA测序(RNA-seq)计数数据。特别是，自然模型参数化消除了归一化步骤的需要，而该方法能够处理涉及多变量相关结构的复杂试验设计。通过新的数据扩充技术，利用条件共轭，获得了模型参数的有效贝叶斯推断。对合成和真实世界RNA-SEQ数据的综合研究表明，在接收器操作特性和精度召回曲线下的区域方面，BNB-R具有优越的性能。可用性和实现：BNB-R用R语言实现，可在https://github.com/siamakz/BNBR.上获得
Motivation: Rapid adoption of high-throughput sequencing technologies has enabled better understanding of genome-wide molecular profile changes associated with phenotypic differences in biomedical studies. Often, these changes are due to multiple interacting factors. Existing methods are mostly considering differential expression across two conditions studying one main factor without considering other confounding factors. In addition, they are often coupled with essential sophisticated ad-hoc pre-processing steps such as normalization, restricting their adaptability to general experimental setups. Complex multi-factor experimental design to accurately decipher genotype-phenotype relationships signifies the need for developing effective statistical tools for genome-scale sequencing data profiled under multi-factor conditions.Results: We have developed a novel Bayesian negative binomial regression (BNB-R) method for the analysis of RNA sequencing (RNA-seq) count data. In particular, the natural model parameterization removes the needs for the normalization step, while the method is capable of tackling complex experimental design involving multi-variate dependence structures. Efficient Bayesian inference of model parameters is obtained by exploiting conditional conjugacy via novel data augmentation techniques. Comprehensive studies on both synthetic and real-world RNA-seq data demonstrate the superior performance of BNB-R in terms of the areas under both the receiver operating characteristic and precision-recall curves.Availability and implementation: BNB-R is implemented in R language and is available at https://github.com/siamakz/BNBR.