Empirical insights into the stochasticity of small RNA sequencing.
Empirical insights into the stochasticity of small RNA sequencing.
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DOI：
10.1038/srep24061
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发表时间：
2016-04-07
影响因子：
4.6
通讯作者：
Singer S
中科院分区：
文献类型：
--
作者：
Qin LX;Tuschl T;Singer S
选择随机分布来模拟噪声分布是测序数据分析的一个基本假设，因此对于准确评估生物异质性和差异表达至关重要。假设RNA测序的随机性遵循泊松分布。我们收集了microRNA测序数据，并观察到其随机性更接近于伽马分布，这可能是因为指数PCR扩增的随机性。我们用两个独立的数据集验证了我们的发现，一个用于microRNA测序，另一个用于RNA测序。在伽玛分布随机性的激励下，我们提供了一种简单的RNA测序数据分析方法，并通过技术重复数据和生物重复数据三个数据实例展示了其相对于现有三种差异表达分析方法的优越性。
The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data.