stepNorm

stepNorm performs stepwise within-slide normalization of cDNA microarray data to detect and adjust systematic errors and enable accurate downstream gene expression analyses.

Key Features:

• Systematic Error Correction: Implements a stepwise within-slide normalization method called STEPNORM to detect and adjust systematic errors in cDNA microarray experiments.
• Sequential Model Integration: Integrates multiple normalization models of varying complexity in a sequential manner to identify and correct systematic variations.
• Spot Intensities: Adjusts intensity-related biases at the spot level.
• Print-Tips: Normalizes variations arising from print-tip effects in the printing process.
• Plates: Accounts for inter-plate differences between individual microarray plates.
• Two-Dimensional Spatial Effects: Corrects two-dimensional spatial artifacts across microarray slides.
• Adaptive Model Selection: Selects the most appropriate normalization model adaptively based on observed data characteristics.

Scientific Applications:

• cDNA microarray normalization: Applies to normalization of cDNA microarray experiments to improve data quality.
• Gene expression profiling: Enables more accurate gene expression profiling by reducing systematic biases.
• Differential expression analysis: Improves reliability of differential expression analysis through systematic error correction.
• Biomarker discovery: Enhances biomarker discovery by improving biological signal detection in normalized datasets.

Methodology:

Implements the STEPNORM stepwise within-slide normalization approach with sequential integration of multiple models of varying complexity, adaptive model selection, and explicit corrections for spot intensities, print-tip effects, plate effects, and two-dimensional spatial artifacts.