Diagnostic Evidence GAuge of Single Cells (DEGAS): a Flexible Deep Transfer Learning Framework for Prioritizing Cells in Relation to Disease

Overview

Journal Genome Med

Publisher Biomed Central

Specialty Genetics

Date 2022 Feb 2

PMID 35105355

Authors

Travis S Johnson

Christina Y Yu

Zhi Huang

Siwen Xu

Tongxin Wang

Chuanpeng Dong

Wei Shao

Mohammad Abu Zaid

Xiaoqing Huang

Yijie Wang

Christopher Bartlett

Yan Zhang

Brian A Walker

Yunlong Liu

Kun Huang

Jie Zhang

Affiliations

Soon will be listed here.

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 myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

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