MBQN

MBQN performs modified quantile normalization for omics or other matrix-like data distorted in location and scale.

Key Features:

• Mean-balanced quantile normalization: Adjusts for location and scale distortions using mean-based balancing during quantile normalization.
• Median-balanced quantile normalization: Adjusts for location and scale distortions using median-based balancing during quantile normalization.

Scientific Applications:

• Omics data normalization: Corrects systematic biases in matrix-like omics data affected by location and scale distortions.

Methodology:

Modified quantile normalization with mean or median balancing to address location and scale distortions in matrix-like data.