OLIN

OLIN performs normalization and systematic error detection for two-color microarray gene expression data to reduce intensity-dependent biases and improve downstream analyses.

Key Features:

• Iterative Local Regression-Based Normalization: Two iterative local regression schemes adjust intensity-dependent biases in two-color microarray datasets.
• Model Selection Integration: Model selection is integrated with normalization to choose optimal local regression models that adapt to within- and between-microarray experiment variability.
• Systematic Error Detection and Visualization: Functions detect and visualize systematic errors and artifacts in two-color microarray data.

Scientific Applications:

• Differential Gene Expression Analysis: Improves the accuracy of differential expression estimates by reducing systematic bias in two-color microarray experiments.
• Biomarker Discovery: Enhances biomarker identification from gene expression profiles by providing robust normalization and error detection.
• Validation and Quality Assessment: Supports validation studies and quality assessment by identifying and visualizing systematic artifacts in microarray datasets.

Methodology:

Normalization using two iterative local regression schemes combined with model selection, plus computational functions for detection and visualization of systematic errors in two-color microarray data.