Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease

Abstract

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19^high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

Keywords: Alzheimer’s disease; Cox proportional hazards; Deep learning; Machine Learning; Multiple myeloma; Prognostic models; Single-cell RNA sequencing; Survival; Transfer learning; scRNA-seq.