SuperFeat:Quantitative Feature Learning from Single-cell RNA-seq Data Facilitates Drug Repurposing  

作　　者：Jianmei Zhong Junyao Yang Yinghui Song Zhihua Zhang Chunming Wang Renyang Tong Chenglong Li Nanhui Yu Lianhong Zou Sulai Liu Jun Pu Wei Lin 

机构地区：[1]State Key Laboratory for Oncogenes and Related Genes,Department of Cardiology,Renji Hospital,School of Medicine,Shanghai Jiao Tong University,Shanghai Cancer Institute,Shanghai 200127,China [2]Department of Laboratory Medicine,Xin Hua Hospital,School of Medicine,Shanghai Jiao Tong University,Shanghai 200092,China [3]Central Laboratory of Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal University,Changsha 410005,China

出　　处：《Genomics, Proteomics & Bioinformatics》2024年第3期91-105,共15页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by grants from the Shanghai Jiao Tong University,the Renji Hospital Start-up funding for New PI,the Natural Science Foundation of Shanghai Science and Technology Innovation Action Plan(Grant No.21ZR1441500);the Young Talent of Hunan(Grant No.2020RC3066);the Hunan Natural Science Fund for Excellent Young Scholars(Grant No.2021JJ20003);the China Postdoctoral Science Foundation(Grant No.2021T140197).

摘　　要：In this study,we devised a computational framework called Supervised Feature Learning and Scoring(SuperFeat)which enables the training of a machine learning model and evaluates the canonical cellular statuses/features in pathological tissues that underlie the progression of disease.This framework also enables the identification of potential drugs that target the presumed detrimental cellular features.This framework was constructed on the basis of an artificial neural network with the gene expression profiles serving as input nodes.The training data comprised single-cell RNA sequencing datasets that encompassed the specific cell lineage during the developmental progression of cell features.A few models of the canonical cancer-involved cellular statuses/features were tested by such framework.Finally,we illustrated the drug repurposing pipeline,utilizing the training parameters derived from the adverse cellular statuses/features,which yielded successful validation results both in vitro and in vivo.SuperFeat is accessible at https://github.com/weilin-genomics/rSuperFeat.

关 键 词：Single-cell transcriptomics Cell state transition Cell scoring Drug search Feature learning 

分 类 号：Q811.4[生物学—生物工程]