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 samplespecific 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
