GS-LAGE

GS-LAGE analyzes large-scale, heterogeneous microarray datasets (including data from NCBI GEO) to characterize gene-specific expression variability and identify selective gene over-expression across platforms.

Key Features:

• Gene-specific variability consideration: Accounts for intrinsic differences in global expressional variability across individual genes to distinguish target-selective expressions from non-selective over-expressions.
• Re-standardization and integration: Re-standardizes and integrates diverse microarray datasets from public repositories to preserve consistent global expression characteristics across experimental conditions and platforms.
• Intrinsic expression parameters: Determines per-gene intrinsic parameters, including global averages and standard deviations (SDs), as consistent benchmarks for rescaling.
• Novel selective expression detection: Uses intrinsic parameters and rescaled data to reveal selective gene expressions that conventional methods may miss, exemplified by detection of cartilage oligomeric matrix protein (COMP) and Collagen X in breast cancer tissues.

Scientific Applications:

• Oncology research: Differentiates selective versus non-selective gene expression patterns to inform disease mechanism studies and potential biomarker identification.
• Breast cancer expression analysis: Identifies tissue-selective over-expressions such as COMP and Collagen X within integrated microarray datasets.
• Cross-platform meta-analysis: Enables comparative analysis of microarray data aggregated from multiple platforms and public databases like NCBI GEO.

Methodology:

Collect and integrate microarray datasets from public databases; re-standardize datasets to account for platform-specific variations; calculate intrinsic expression parameters (global averages and SDs) for each gene; rescale data using these parameters to highlight selective gene expressions.