MANOR

MANOR normalizes and corrects spatial and technical biases in array-CGH (comparative genomic hybridization) data to improve detection of DNA copy-number changes.

Key Features:

• Normalization Techniques: Targets preservation of genuine DNA copy-number signals while correcting experimental artifacts in microarray-based CGH data.
• Spatial Normalization: Identifies and corrects spatially biased regions through spatial trend estimation and spatial segmentation.
• Spatial Segmentation (NEM): Uses the Neighborhood Expectation Maximization (NEM) spatial segmentation algorithm to delineate and correct local spatial bias and continuous spatial gradients.
• Quality Control and Validation: Establishes quality-control criteria to reduce artifact-driven outliers, demonstrated on three datasets from two platforms comprising 198, 175, and 26 BAC-arrays.

Scientific Applications:

• Cancer genomics: Enhances accuracy of DNA copy-number alteration detection in tumor profiling using array-CGH.
• Genetic disorder studies: Improves reliability of copy-number variant identification in clinical and research analyses.
• Evolutionary biology: Supports comparative analyses of genome copy-number variation across species or populations.

Methodology:

Computational steps explicitly include importation of array-CGH data, spatial trend estimation, spatial segmentation with Neighborhood Expectation Maximization (NEM), normalization to correct continuous spatial gradients and local spatial bias, visualization, and application of quality-control criteria.