XPN

XPN normalizes and integrates gene-expression datasets derived from different technological platforms to enable cross-study analyses of a common organism and phenotype.

Key Features:

• Cross-Study Normalization Method: Uses linked gene/sample clustering to harmonize gene-expression data across disparate platforms.
• Validation Measures: Provides validation metrics to assess and compare the effectiveness of cross-study normalization methods while preserving biological signals.
• Application to Breast Cancer Datasets: Has been applied to three breast cancer gene-expression datasets to demonstrate integration across studies.
• Comparative Analysis: Performs comparative evaluations against several competing normalization methods using the proposed validation measures.

Scientific Applications:

• Oncology (breast cancer): Enables integration of breast cancer gene-expression datasets from multiple platforms for cross-study analyses.
• Meta-analysis of gene-expression studies: Facilitates combined analyses of datasets across studies and platforms.
• Large-scale genomic studies: Supports aggregation of gene-expression data from diverse platforms to improve generalizability of findings.

Methodology:

Normalization via linked gene/sample clustering to align expression profiles across platforms, with validation measures used to evaluate integration quality.