BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data

We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect differentially expressed genes by comparing the posterior distributions of gene-specific negative binomial dispersion (probability) parameters. These model parameters are inferred by borrowing statistical strength across both the genes and samples. Extensive experiments on both simulated and real-world RNA sequencing count data show that the proposed differential expression analysis algorithms clearly outperform previously proposed ones in terms of the areas under both the receiver operating characteristic and precision-recall curves. Supplementary materials for this article are available online.

本研究在贝叶斯非参数（Bayesian nonparametric）框架下开展高通量测序计数数据的差异表达分析，规避了现有算法中普遍所需的复杂特设预处理流程。本研究提出采用伽马（贝塔）负二项过程（gamma (beta) negative binomial process），通过引入样本特异性负二项概率（离散度）参数适配不同测序深度，通过对比基因特异性负二项离散（概率）参数的后验分布来检测差异表达基因。该模型参数通过跨基因与样本的统计信息共享机制完成推断。针对模拟数据集与真实RNA测序（RNA sequencing）计数数据开展的大规模实验表明，本研究提出的差异表达分析算法在受试者工作特征（receiver operating characteristic）曲线与精确率-召回率（precision-recall）曲线的曲线下面积指标上均显著优于此前提出的同类算法。本文补充材料可在线获取。