Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences

Natalie S. Fox, Maud H. W. Starmans, Syed Haider, Philippe Lambin, Paul C. Boutros*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Web of Science)

Abstract

Background: The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms. Methods: To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients). Results: We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures. Conclusions: Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
Original languageEnglish
Article number170
JournalBMC Bioinformatics
Volume15
DOIs
Publication statusPublished - 6 Jun 2014

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