An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.
An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.
复制标题
用于 RNA-seq 差异表达分析的对个体异质性稳健的经验似然比检验。
DOI：
10.1093/bib/bbw103
复制
发表时间：
2018
影响因子：
9.5
通讯作者：
Chen,Liang
中科院分区：
文献类型：
--
作者：
Xu,Maoqi;Chen,Liang
个体样本的异质性是癌症等复杂疾病生物标志物鉴定的最大障碍之一。目前的统计模型，以确定疾病和对照组之间的差异表达的基因往往忽略了大量的人类样本的异质性。同时，传统的非参数检验虽然具有分布自由性和对异质性的鲁棒性，但却丢失了数据的细节信息，牺牲了分析能力。在这里，我们提出了一个经验似然比检验与均值-方差关系约束（ELTSeq）的差异表达分析的RNA测序（RNA-seq）。作为一种无分布的非参数模型，ELTSeq通过估计每个观察结果的经验概率来处理个体异质性，而不对读数分布进行任何假设。它还结合了对读取计数过度分散的约束，这在RNA-seq数据中被广泛观察到。ELTSeq在处理异质性组时表现出对现有方法的显着改进，例如edgeR，DESeq，t检验，Wilcoxon检验和经典的经验似然比检验。它将大大推进癌症和其他复杂疾病的转录组学研究。
The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean–variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without making any assumption about read-count distribution. It also incorporates a constraint for the read-count overdispersion, which is widely observed in RNA-seq data. ELTSeq demonstrates a significant improvement over existing methods such as edgeR, DESeq,t-tests, Wilcoxon tests and the classic empirical likelihood-ratio test when handling heterogeneous groups. It will significantly advance the transcriptomics studies of cancers and other complex disease.