Conditional quantile normalization

Conditional quantile normalization normalizes RNA-Seq read counts by adjusting for sample-specific biases, particularly GC-content, and applying quantile normalization to reduce unwanted variability in gene expression measurements.

Key Features:

• Reduction of Variability: Mitigates unwanted sources of variability in RNA-Seq data similar to those observed in microarray experiments.
• GC-Content Adjustment: Corrects sample-specific biases introduced by guanine-cytosine (GC) content that affect gene expression measurements.
• Robust Generalized Regression: Uses robust generalized regression to model and remove systematic bias associated with deterministic features such as GC-content.
• Quantile Normalization: Applies quantile normalization to correct global distributional distortions across samples.

Scientific Applications:

• Enhanced Precision in Gene Expression Studies: Improves precision of RNA-Seq analyses (reported improvement of 42%), enabling more reliable differential expression measurement.
• Reduction of False Positives: Adjusting for GC-content-related biases reduces false positives in downstream analyses.
• Broad Applicability in Molecular Biology Research: Applicable to genome-wide gene expression studies that require normalization of RNA-Seq data.

Methodology:

CQN fits robust generalized regression models to adjust for deterministic features such as GC-content (conditional normalization) and then applies quantile normalization to align expression distributions across samples.