GBMPurity: A machine learning tool for estimating glioblastoma tumor purity from bulk RNA-sequencing data.

Journal: Neuro-oncology
Published Date: Jul 30, 2025

Abstract

BACKGROUND: Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumor purity, the proportion of malignant cells within a tumor, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumor purity are nonspecific and technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM.

Authors

  • Morgan P H Thomas
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Shoaib Ajaib
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Georgette Tanner
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
  • Andrew J Bulpitt
    School of Computer Science, University of Leeds, Leeds, UK.
  • Lucy F Stead
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.

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