InterOpt

InterOpt improves gene expression quantification accuracy in quantitative PCR (qPCR) experiments by introducing scale-invariant reference gene aggregation methods for robust normalization.

Key Features:

• Normalization Enhancement: Refines qPCR normalization by addressing limitations of using the geometric mean of multiple study-specific reference genes (RGs) across samples.
• Scale-Invariant Aggregation Functions: Introduces a family of scale-invariant functions as alternatives to the conventional geometric mean for reference gene aggregation.
• Weighted Geometric Mean Minimizing Standard Deviation: Implements a candidate method that computes a weighted geometric mean optimized to minimize the standard deviation of aggregated reference gene expression.
• Theoretical and Experimental Validation: Methodology is supported by theoretical analysis and empirical application to datasets including solid tumors and liquid biopsies.
• Efficient Computation: Provides closed-form solutions and regression-based methods for computation and supports GPU acceleration for high-throughput processing.
• R Package Implementation: All proposed methods are implemented in an R package for integration into bioinformatics workflows.

Scientific Applications:

• Oncology — Solid Tumors and Liquid Biopsies: Improves normalization in qPCR studies of oncology samples, including analyses of solid tumors and liquid biopsies.
• High-Throughput and Cross-Condition qPCR Studies: Enables more robust cross-sample and cross-condition normalization in large-scale qPCR experiments.

Methodology:

Implements scale-invariant aggregation functions and a weighted geometric mean that minimizes standard deviation, computed via closed-form solutions or regression-based methods with optional GPU acceleration.