AbstractWith the rapid advance of single cell sequencing techniques, single cell molecular data are quickly accumulated. However, there lacks a sound approach to properly integrate single cell data with the existing large amount of patient-level disease data. To address such need, we proposed DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer-learning framework which allows for cellular and clinical information, including cell types, disease risk, and patient subtypes, to be cross-mapped between single cell and patient data, provided they share at least one common type of molecular data. We call such transferrable information “impressions”, which are generated by the deep learning models learned in the DEGAS framework. Using eight datasets from a wide range of diseases including Glioblastoma Multiforme (GBM), Alzheimer’s Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility and broad applications of DEGAS in cross-mapping clinical and cellular information across disparate single cell and patient level transcriptomic datasets. Specifically, we correctly mapped clinically known GBM patient subtypes onto single cell data. We also identified previously known neuron loss from AD brains, then mapped the “impression” of AD risk to single cell data. Furthermore, we discovered novel differences in excitatory and inhibitory neuron loss in AD data. From the exploratory MM data, we identified differences in the malignancy of different CD138+ cellular subtypes based on “impressions” of relapse information transferred from MM patients. Through this work, we demonstrated that DEGAS is a powerful framework to cross-infer cellular and patient-level characteristics, which not only unites single cell and patient level transcriptomic data by identifying their latent links using the deep learning approach, but can also prioritize both patient subtypes and cellular subtypes for precision medicine.
A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification
