Predicting lung aging using scRNA-Seq data. - Qi Song; Alex Singh; John E McDonough; Taylor S Adams; Robin Vos; Ruben De Man; Greg Myers; Laurens J Ceulemans; Bart M Vanaudenaerde; Wim A Wuyts; Xiting Yan; Jonas Schupp; et al. (PLoS Computational Biology)
Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers.