BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

• Authors: Saba Ghaffari, University of Illinois at Urbana-Champaign; Kelly J Bouchonville, Mayo Clinic; Ehsan Saleh, University of Illinois at Urbana-Champaign; Remington E Schmidt, Mayo Clinic; Steven M Offer, Mayo Clinic; Saurabh Sinha, Emory University,
• Journal Title: GENOME BIOLOGY
• Volume: Volume 24, Number 1
• Publisher: BMC | 2023-08-03, Pages 178-178
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1186/s13059-023-03007-7
• Subjects: Computer Science; Health Sciences, Pharmacology; Engineering, Biomedical
• Keywords: Science & Technology; Life Sciences & Biomedicine; Biotechnology & Applied Microbiology; Genetics & Heredity; Bulk gene expression deconvolution; Single cell RNA-seq; Bayesian inference; Dihydropyridine dehydrogenase deficiency; DIHYDROPYRIMIDINE DEHYDROGENASE-DEFICIENCY; PRIMARY CILIA; INTEGRATIVE ANALYSIS; CELL-TYPE; TRANSCRIPTOME; NEURONS

Abstract

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.