noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

Abstract

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. Teaser Noise removal from sequencing quantification improves the convergence of downstream tools and robustness of conclusions.