High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimising technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.

中文翻译：

---

noisyR：通过表征随机技术噪声来增强测序数据集中的生物信号

高通量测序可实现转录定量的前所未有的分辨率，但要以扩大技术噪音的影响为代价。持续降低随机背景噪声以捕获对功能有意义的生物信号仍然具有挑战性。引入低水平表达变异的内在测序变异会掩盖下游分析中的模式。我们引入了noisyR，这是一种全面的噪声滤波器，用于评估信号分布的变化并在重复样本之间获得最佳的信息一致性。此选择还有助于在背景噪声范围之外进行有意义的模式识别。noisyR适用于计数矩阵和测序数据；它输出特定于样本的信号/噪声阈值和经过过滤的表达矩阵。我们举例说明了在整个测序和单细胞水平上，在各种测序测定中，在多个数据集上最小化技术噪声的影响：编码，非编码RNA和相互作用。滤除噪声的直接后果是跨不同方法的预测（差异表达调用，富集分析和基因调控网络推论）的融合。