Abstract
Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.
Original language | English |
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Article number | 96 |
Number of pages | 15 |
Journal | Genome medicine |
Volume | 10 |
DOIs | |
Publication status | Published - 19-Dec-2018 |
Keywords
- SINGLE-CELL
- RNA-SEQ
- EXPRESSION
- INFERENCE
- TIME
- VARIANTS
- IDENTIFICATION
- CIRCUITS
- TRANSCRIPTION
- ENCYCLOPEDIA
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Additional file 11 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-Apr-2023
DOI: 10.6084/m9.figshare.22655429.v1, https://springernature.figshare.com/articles/dataset/Additional_file_11_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655429/1 and one more link, https://springernature.figshare.com/articles/dataset/Additional_file_11_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655429 (show fewer)
Dataset
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Additional file 7 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-Apr-2023
DOI: 10.6084/m9.figshare.22655417.v1, https://springernature.figshare.com/articles/dataset/Additional_file_7_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655417/1
Dataset
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Additional file 1 of Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data
Li, S. (Creator), Schmid, K. T. (Creator), Vries ,de, D. (Creator), Korshevniuk, M. (Creator), Losert, C. (Creator), Oelen, R. (Creator), van Blokland, I. (Creator), Groot-Nauta, H. (Creator), Swertz, M. (Creator), van der Harst, P. (Creator), Westra, H. J. (Creator), van der Wijst, M. G. P. (Creator), Heinig, M. (Creator) & Franke, L. (Creator), figshare, 18-Apr-2023
DOI: 10.6084/m9.figshare.22655396, https://springernature.figshare.com/articles/dataset/Additional_file_1_of_Identification_of_genetic_variants_that_impact_gene_co-expression_relationships_using_large-scale_single-cell_data/22655396
Dataset