Johnson, Travis S.Yu, Christina Y.Huang, ZhiXu, SiwenWang, TongxinDong, ChuanpengShao, WeiZaid, Mohammad AbuHuang, XiaoqingWang, YijieBartlett, ChristopherZhang, YanWalker, Brian A.Liu, YunlongHuang, KunZhang, Jie2023-05-022023-05-022022-02-01Johnson TS, Yu CY, Huang Z, et al. Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease. Genome Med. 2022;14(1):11. Published 2022 Feb 1. doi:10.1186/s13073-022-01012-2https://hdl.handle.net/1805/32770We 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 PHF19high myeloma cells associated with progression.en-USAttribution 4.0 InternationalAlzheimer’s diseaseCox proportional hazardsDeep learningMachine learningMultiple myelomaPrognostic modelsSingle-cell RNA sequencingSurvivalTransfer learningscRNA-seqArticle