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InterOpt: Improved gene expression quantification in qPCR experiments using weighted aggregation of reference genes.

Adel Salimi1, Saeid Rahmani1,2, Ali Sharifi-Zarchi1

  • 1Computer Engineering Department, Sharif University of Technology, Tehran 11155-1639, Tehran, Iran.

Iscience
|October 13, 2023
PubMed
Summary
This summary is machine-generated.

Quantitative PCR (qPCR) accuracy relies on reference gene (RG) normalization. This study introduces improved RG aggregation methods, outperforming the standard geometric mean for precise gene expression analysis.

Keywords:
Biocomputational methodData processing in systems biologyExpression study

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Quantitative PCR (qPCR) is the gold standard for gene expression quantification.
  • Accurate normalization using reference genes (RGs) is critical for qPCR reliability.
  • Current methods for aggregating multiple RGs often lack optimal performance.

Purpose of the Study:

  • To introduce novel, scale-invariant functions for aggregating multiple reference genes in qPCR.
  • To compare the performance of these new aggregation methods against the conventional geometric mean.
  • To provide a computationally efficient and adaptable solution for reference gene normalization.

Main Methods:

  • Development of a family of scale-invariant functions for RG aggregation.
  • Evaluation of a weighted geometric mean method that minimizes standard deviation.
  • Theoretical analysis and experimental validation using real-world data from solid tumors and liquid biopsies.
  • Implementation of proposed methods in an R package with GPU acceleration.

Main Results:

  • The proposed weighted geometric mean method demonstrated superior performance compared to existing aggregation techniques.
  • Scale-invariant functions offer a robust alternative to the geometric mean for RG normalization.
  • The developed R package enables efficient and accessible computation of normalized gene expression data.

Conclusions:

  • Novel RG aggregation methods significantly enhance qPCR accuracy.
  • The weighted geometric mean minimizing standard deviation is a highly effective approach.
  • The provided R package facilitates the widespread adoption of improved normalization strategies in gene expression studies.