RT Journal Article
SR Electronic
T1 Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 837807
DO 10.1101/837807
A1 Haripriya Harikumar
A1 Thomas P. Quinn
A1 Santu Rana
A1 Sunil Gupta
A1 Svetha Venkatesh
YR 2021
UL http://biorxiv.org/content/early/2021/01/11/837807.abstract
AB Background The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient.Methods We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses.Conclusions Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.Competing Interest StatementThe authors have declared no competing interest.RWrandom walkRWRrandom walk with restartRNA-SeqRNA sequencingMSEmean square errorscRNA-Seqsingle-cell RNA sequencingLINCSLibrary of Integrated Network-Based Cellular SignaturesGOGene OntologyBPBiological ProcessMFMolecular FunctionANOVAanalysis of variance