AXOLOTL: an accurate method for detecting aberrant gene expression in rare diseases using coexpression constraints

Abstract

BackgroundThe assessment of aberrant transcription events in patients with rare diseases holds promise for significantly enhancing the prioritization of causative genes, a practice already widely employed in clinical settings to increase diagnostic accuracy. Nevertheless, the entangled correlation between genes presents a substantial challenge for accurate identification of causal genes in clinical diagnostic scenarios. Currently, none of the existing methods are capable of effectively modeling gene correlation. MethodsWe propose a novel unsupervised method, AXOLOTL, to identify aberrant gene expression events in an RNA expression matrix. AXOLOTL effectively addresses biological confounders by incorporating coexpression constraints. ResultsWe demonstrated the superior performance of AXOLOTL on representative RNA-seq datasets, including those from the GTEx healthy cohort, mitochondrial disease cohort and Collagen VI-related dystrophy cohort. Furthermore, we applied AXOLOTL to real case studies and demonstrated its ability to accurately identify aberrant gene expression and facilitate the prioritization of pathogenic variants.