De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters. / Oubounyt, Mhaned; Elkjaer, Maria L.; Laske, Tanja; Grønning, Alexander G.B.; Moeller, Marcus J; Baumbach, Jan.

In: NAR Genomics and Bioinformatics, Vol. 5, No. 1, lqad018, 2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Oubounyt, M, Elkjaer, ML, Laske, T, Grønning, AGB, Moeller, MJ & Baumbach, J 2023, 'De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters', NAR Genomics and Bioinformatics, vol. 5, no. 1, lqad018. https://doi.org/10.1093/nargab/lqad018

APA

Oubounyt, M., Elkjaer, M. L., Laske, T., Grønning, A. G. B., Moeller, M. J., & Baumbach, J. (2023). De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters. NAR Genomics and Bioinformatics, 5(1), [lqad018]. https://doi.org/10.1093/nargab/lqad018

Vancouver

Oubounyt M, Elkjaer ML, Laske T, Grønning AGB, Moeller MJ, Baumbach J. De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters. NAR Genomics and Bioinformatics. 2023;5(1). lqad018. https://doi.org/10.1093/nargab/lqad018

Author

Oubounyt, Mhaned ; Elkjaer, Maria L. ; Laske, Tanja ; Grønning, Alexander G.B. ; Moeller, Marcus J ; Baumbach, Jan. / De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters. In: NAR Genomics and Bioinformatics. 2023 ; Vol. 5, No. 1.

Bibtex

@article{b9d520e2e41743b6b1eb0631e198319f,
title = "De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters",
abstract = "Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.",
author = "Mhaned Oubounyt and Elkjaer, {Maria L.} and Tanja Laske and Gr{\o}nning, {Alexander G.B.} and Moeller, {Marcus J} and Jan Baumbach",
note = "{\textcopyright} The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.",
year = "2023",
doi = "10.1093/nargab/lqad018",
language = "English",
volume = "5",
journal = "NAR Genomics and Bioinformatics",
issn = "2631-9268",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters

AU - Oubounyt, Mhaned

AU - Elkjaer, Maria L.

AU - Laske, Tanja

AU - Grønning, Alexander G.B.

AU - Moeller, Marcus J

AU - Baumbach, Jan

N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

PY - 2023

Y1 - 2023

N2 - Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.

AB - Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.

U2 - 10.1093/nargab/lqad018

DO - 10.1093/nargab/lqad018

M3 - Journal article

C2 - 36879901

VL - 5

JO - NAR Genomics and Bioinformatics

JF - NAR Genomics and Bioinformatics

SN - 2631-9268

IS - 1

M1 - lqad018

ER -

ID: 340363679