Centres for Doctoral Training

Redefining qRT-PCR normalisation and imputation methods via innovative advanced statistical techniques

Supervisors: 

Dr John McClure, College of Medical Veterinary and Life Sciences, School of Cardiovascular and Metabolic Health 

Scott MacKenzie, School of Cardiovascular and Metabolic Health (SCMH), College of Medical Veterinary and Life Sciences

Vincent Macaulay, School of Mathematics and Statistics (SoMaS), College of Science and Engineering

Mayetri Gupta, School of Mathematics and Statistics (SoMaS), College of Science and Engineering

PHD Project Summary:

This project offers an exciting opportunity to advance the accuracy and reliability of RNA quantification by developing and testing novel computational statistics methods. Quantitative real-time reverse transcription PCR (qRT-PCR) is the gold standard for measuring gene expression, yet its most widely used normalisation method—Livak and Schmittgen’s ΔCt approach—has never been rigorously evaluated. This project will critically assess the statistical assumptions behind ΔCt, explore alternative statistical normalisation strategies, and develop robust frameworks for reference gene selection, particularly for emerging targets like microRNAs.

The student will also design and evaluate imputation models to handle missing data, a common challenge in qRT-PCR studies. These models will distinguish between biological absence and technical error, improving data integrity in large-scale biological and clinical datasets.

Working with data from multiple qRT-PCR experiments, including a large diagnostics study of more than 170 genes across 400 participants, the student will gain hands-on experience in advanced statistical modelling, computational biology, and Bayesian methods. Students will gain methodological and practical skills vital to wider statistical and data science applications.

This project is ideal for candidates passionate about statistically rigorous data-driven biological and medical research who are eager to shape the future of this research through methodological innovation.