Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient
Abstract 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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | BioData Mining |
| Online Access: | https://doi.org/10.1186/s13040-021-00263-w |
| _version_ | 1818365450255335424 |
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| author | Haripriya Harikumar Thomas P. Quinn Santu Rana Sunil Gupta Svetha Venkatesh |
| author_facet | Haripriya Harikumar Thomas P. Quinn Santu Rana Sunil Gupta Svetha Venkatesh |
| author_sort | Haripriya Harikumar |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-13T22:20:27Z |
| format | Article |
| id | doaj.art-862b0a4f44314d8e9aded21e456593da |
| institution | Directory Open Access Journal |
| issn | 1756-0381 |
| language | English |
| last_indexed | 2024-12-13T22:20:27Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj.art-862b0a4f44314d8e9aded21e456593da2022-12-21T23:29:24ZengBMCBioData Mining1756-03812021-08-0114111510.1186/s13040-021-00263-wPersonalized single-cell networks: a framework to predict the response of any gene to any drug for any patientHaripriya Harikumar0Thomas P. Quinn1Santu Rana2Sunil Gupta3Svetha Venkatesh4Applied Artificial Intelligence Institute, Deakin UniversityApplied Artificial Intelligence Institute, Deakin UniversityApplied Artificial Intelligence Institute, Deakin UniversityApplied Artificial Intelligence Institute, Deakin UniversityApplied Artificial Intelligence Institute, Deakin UniversityAbstract 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.https://doi.org/10.1186/s13040-021-00263-w |
| spellingShingle | Haripriya Harikumar Thomas P. Quinn Santu Rana Sunil Gupta Svetha Venkatesh Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient BioData Mining |
| title | Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient |
| title_full | Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient |
| title_fullStr | Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient |
| title_full_unstemmed | Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient |
| title_short | Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient |
| title_sort | personalized single cell networks a framework to predict the response of any gene to any drug for any patient |
| url | https://doi.org/10.1186/s13040-021-00263-w |
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