cqn

cqn performs conditional quantile normalization to correct systematic biases such as guanine-cytosine (GC) content and global distributional differences in RNA-seq data to improve gene expression measurements.

Key Features:

• Conditional Quantile Normalization Methodology: Implements conditional quantile normalization to adjust for covariate-dependent biases in RNA-seq data.
• Systematic Bias Correction: Uses robust generalized regression techniques to remove biases associated with guanine-cytosine (GC) content.
• Global Distortion Adjustment: Applies quantile normalization to correct global distributional differences across samples.
• Improved Precision and Accuracy: Has been shown to enhance precision by 42% without compromising accuracy.

Scientific Applications:

• Gene Expression Analysis: Provides normalized RNA-seq data for more accurate gene expression profiling across conditions or treatments.
• Differential Expression Studies: Reduces false positives in comparative analyses to support identification of truly differentially expressed genes.
• Genomic Research: Removes systematic biases from RNA-seq datasets to improve validity of downstream genomic inferences.

Methodology:

Conditional quantile normalization using robust generalized regression to remove GC-content effects, followed by quantile normalization to adjust global distributional differences.