iRGvalid

iRGvalid identifies and ranks stable reference genes for normalization of relative gene expression quantification using an in silico double-normalization and statistical stability assessment.

Key Features:

• In Silico Validation: Operates entirely within a computational framework, eliminating the need for wet-lab validation.
• Double-Normalization Strategy: Performs two-step normalization by first normalizing each gene's expression against total gene expression per sample and then normalizing target genes relative to candidate reference genes.
• Linear Regression Analysis: Uses linear regression to compare pre- and post-normalized target gene expressions across samples to assess reference gene stability.
• Pearson Correlation Coefficient (Rt): Quantifies the stability of each reference gene using the Pearson correlation coefficient Rt, with higher Rt values indicating greater stability.
• Combination Analysis: Evaluates individual genes and all possible combinations of candidate reference genes, typically identifying optimal combinations of 3 to 6 genes.
• High-throughput Data Utilization: Leverages high-throughput gene expression datasets for comprehensive stability evaluation.

Scientific Applications:

• Cancer Reference Gene Evaluation: Applied to evaluate 14 candidate reference genes across lung adenocarcinoma, breast cancer, colon adenocarcinoma, and nasopharyngeal cancer.
• Identification of Optimal Normalization Sets: Identifies highly stable single genes and gene combinations for normalization in gene expression studies.
• Cross-study Comparison: Enables direct comparison of reference gene stability across large datasets and different studies.

Methodology:

Uses high-throughput gene expression data; normalizes each gene to total sample expression then normalizes target genes to candidate reference genes; compares pre- and post-normalization target expressions by linear regression and quantifies stability with the Pearson correlation coefficient Rt; evaluates all possible combinations of candidate genes (often 3–6 genes) to identify those with highest stability.