: A mixture model for studying the function of ACE2 based on bulk proteogenomic data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose , a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. decomposes the data and models cell-type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and utilizes a non-parametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate has well-controlled false discovery rates and favorable powers in non-asymptotic settings. We apply to the proteomic data of 110 (tumor adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon / response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies.