noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing offers high-resolution transcript quantification but amplifies technical noise.
• Distinguishing true biological signals from background noise remains a significant challenge.
• Low-level expression variations from sequencing variability can obscure biological patterns.

Purpose of the Study:

• To introduce noisyR, a comprehensive noise filter for sequencing data.
• To enable consistent signal assessment and optimal information-consistency across replicates and samples.
• To facilitate meaningful pattern recognition by reducing background noise.

Main Methods:

• noisyR assesses signal distribution variation and identifies sample-specific signal/noise thresholds.
• The tool filters count matrices and sequencing data, outputting filtered expression matrices.
• Application across diverse sequencing assays (coding, non-coding RNAs, interactions) at bulk and single-cell levels.

Main Results:

• Minimizing technical noise leads to improved signal-to-noise ratios.
• noisyR enhances information-consistency across replicates and samples.
• Filtered data improves the convergence of downstream analytical predictions.

Conclusions:

• noisyR is a valuable tool for reducing technical noise in transcriptomic data.
• Effective noise reduction enhances the reliability of biological signal detection.
• Filtering noise improves the accuracy and consistency of differential expression, enrichment, and network inference analyses.