noiseDetection_mRNA

noiseDetection_mRNA detects and mitigates technical noise in bulk RNA sequencing (RNA-seq) datasets to improve the accuracy of differential gene expression (DE) analyses and reduce spurious signals from low-abundance transcripts.

Key Features:

• Noise Identification: Evaluates sequence-derived noise via correlations of expression distributions across transcripts and analytical noise via comparisons among standard read-processing tools.
• Simulated Datasets: Uses simulated datasets that mimic human (H.sapiens) and mouse (M.musculus) RNA-seq characteristics to illustrate technical noise under differing inter-individual variability.
• Noise-Range Analysis: Identifies the proportion of genes affected by noise for each combination of sequencing and analytical tools to quantify how setups influence DE calls.
• Data-Driven Noise Thresholds: Applies sample-specific, data-driven thresholds to minimize the impact of low-level variations and refine DE gene identification.
• Convergence Across Tools: Reduces the number of significantly differentially expressed genes and promotes convergence in DE calls across various sequencing and processing tool combinations.

Scientific Applications:

• Enhanced Accuracy in DE Analysis: Reduces technical noise to improve the reliability of differential gene expression results.
• Improved Interpretation of Results: Helps distinguish true biological signals from artifacts introduced by sequencing technologies or analytical methods.
• Cross-Species Comparisons: Applicable to comparative studies using datasets with varying inter-individual variability, including H.sapiens and M.musculus.

Methodology:

Generates and analyzes simulated RNA-seq datasets, assesses noise via correlations of expression distributions and comparisons among read-processing tools, and applies sample-specific data-driven thresholds to adjust for biases.