noisyR

noisyR removes technical noise from high-throughput sequencing data to improve the resolution and reliability of transcript quantification.

Key Features:

• Noise Quantification and Removal: Quantifies and removes technical noise using two methodologies: a count matrix-based approach and direct manipulation of alignment BAM files.
• Signal/Noise Thresholds: Generates sample-specific signal/noise thresholds tailored to each dataset.
• Filtered Expression Matrices: Outputs filtered expression matrices devoid of technical noise for downstream analyses.
• Comprehensive Noise Filtering: Assesses variation in signal distribution to achieve information consistency across replicates and samples.
• Versatility Across Assays: Applies to assays of coding and non-coding RNAs and to both bulk and single-cell sequencing data.

Scientific Applications:

• Enhanced Pattern Recognition: Reduces technical noise to improve concordance of differential expression calls, enrichment analyses, and gene regulatory network inference across analytical approaches.
• Improved Data Consistency: Stabilizes signal distributions to facilitate reliable biological interpretation across replicates and samples.

Methodology:

Quantifies noise by evaluating variation in signal distribution, implements noise removal via count matrix-based and alignment BAM file-based methods, generates sample-specific signal/noise thresholds, and outputs filtered expression matrices.