Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet
Аутори
Oubouny, MhanedBaumbach, Jan
L. Elkjaer, Maria
Остала ауторства
Morić, IvanaĐorđević, Valentina
Конференцијски прилог (Објављена верзија)
,
© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
Метаподаци
Приказ свих података о документуАпстракт
Differences in co-expression networks between two or multiple cell (sub)types across
conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key
challenge is to define those co-variations that differ between or among cell types and/
or conditions and phenotypes to examine small regulatory networks that can explain
mechanistic differences. To this end, we developed SCANet, an all-in-one Python
package that uses state-of-the-art algorithms to facilitate the workflow of a combined
single-cell GCN and GRN pipeline including inference of gene co-expression modules
from scRNA-seq, followed by trait and cell type associations, hub gene detection, coregulatory
networks, and drug-gene interactions. To illustrate the power of SCANet, we
examined data from two studies. First, we identify the drivers of the mechanotype of
a cytokine storm associated with increased mortality in patients with acute respiratory
illness. Secondly, we find 20 drugs for 8 potential pharm...acological targets in cellular driver
mechanisms in the intestinal stem cells of obese mice. SCANet is available as a free, open
source, and user-friendly Python package that can be easily integrated in systems biology
pipelines.
Кључне речи:
small single cell networks / GRN / GCN / mechanotyping / drug repurposingИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 50-50Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Oubouny, Mhaned AU - Baumbach, Jan AU - L. Elkjaer, Maria PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1992 AB - Differences in co-expression networks between two or multiple cell (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/ or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN and GRN pipeline including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, coregulatory networks, and drug-gene interactions. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is available as a free, open source, and user-friendly Python package that can be easily integrated in systems biology pipelines. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet EP - 50 SP - 50 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1992 ER -
@conference{ author = "Oubouny, Mhaned and Baumbach, Jan and L. Elkjaer, Maria", year = "2023", abstract = "Differences in co-expression networks between two or multiple cell (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/ or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN and GRN pipeline including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, coregulatory networks, and drug-gene interactions. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. SCANet is available as a free, open source, and user-friendly Python package that can be easily integrated in systems biology pipelines.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet", pages = "50-50", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1992" }
Oubouny, M., Baumbach, J.,& L. Elkjaer, M.. (2023). Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 50-50. https://hdl.handle.net/21.15107/rcub_imagine_1992
Oubouny M, Baumbach J, L. Elkjaer M. Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet. in 4th Belgrade Bioinformatics Conference. 2023;4:50-50. https://hdl.handle.net/21.15107/rcub_imagine_1992 .
Oubouny, Mhaned, Baumbach, Jan, L. Elkjaer, Maria, "Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet" in 4th Belgrade Bioinformatics Conference, 4 (2023):50-50, https://hdl.handle.net/21.15107/rcub_imagine_1992 .