RT Journal Article
SR Electronic
T1 A random walk down personalized single-cell networks: predicting 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 2019
UL http://biorxiv.org/content/early/2019/11/11/837807.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 the gene expression of 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 use a “dual-channel” random walk with restart (RWR) algorithm to perform 3 analyses. First, we use glioblastoma single cells from 5 individual patients to discover genes whose functions differ between cancers. Second, we use drug screening data from the Library of Integrated Network-Based Cellular Signatures (LINCS) to show how a cell-specific drug-response signature can be accurately predicted from a baseline (drug-free) gene co-expression network. Finally, we combine both data streams to show how the RWR algorithm can predict how any gene will respond to any drug for each of the 5 glioblastoma patients.Conclusions Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” RWR 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. When applied to real data, we identify a number of genes that exhibit a patient-specific drug response, including the pan-cancer oncogene EGFR.RWrandom walkRWRrandom walk with restartRNA-SeqRNA sequencingscRNA-Seqsingle-cell RNA sequencingLINCSLibrary of Integrated Network-Based Cellular SignaturesGOGene OntologyBPBiological ProcessMFMolecular FunctionANOVAanalysis of variance