OLINgui

OLINgui normalizes microarray data using iterative local regression and model selection to reduce systematic errors and improve the reliability of downstream gene expression analyses.

Key Features:

• Iterative local regression normalization: Two normalization schemes employ iterative local regression combined with model selection to adaptively adjust normalization parameters to the specific structure and variability of each microarray dataset.
• Systematic error detection and visualization: Functions identify and visualize systematic errors and biases within microarray datasets to inform correction and downstream analysis.

Scientific Applications:

• Genomics: Improves normalization of microarray-derived genomic signals to support comparative analyses and biomarker discovery.
• Transcriptomics: Enhances accuracy of gene expression measurements from microarray experiments for differential expression and expression profiling.
• Systems biology: Reduces systematic bias in microarray inputs used for network inference, pathway analysis, and integrative modeling.
• Gene expression analysis: Provides robust normalization to increase reliability of downstream statistical analyses of microarray-based expression data.

Methodology:

The normalization approach uses iterative local regression combined with model selection to adjust normalization parameters according to dataset-specific characteristics.