Single-cell gene regulatory network prediction by explainable AI
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Single-cell gene regulatory network prediction by explainable AI. / Keyl, Philipp; Bischoff, Philip; Dernbach, Gabriel; Bockmayr, Michael; Fritz, Rebecca; Horst, David; Blüthgen, Nils; Montavon, Grégoire; Müller, Klaus-Robert; Klauschen, Frederick.
in: NUCLEIC ACIDS RES, Jahrgang 51, Nr. 4, 28.02.2023, S. e20.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Single-cell gene regulatory network prediction by explainable AI
AU - Keyl, Philipp
AU - Bischoff, Philip
AU - Dernbach, Gabriel
AU - Bockmayr, Michael
AU - Fritz, Rebecca
AU - Horst, David
AU - Blüthgen, Nils
AU - Montavon, Grégoire
AU - Müller, Klaus-Robert
AU - Klauschen, Frederick
N1 - © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.
PY - 2023/2/28
Y1 - 2023/2/28
N2 - The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
AB - The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
U2 - 10.1093/nar/gkac1212
DO - 10.1093/nar/gkac1212
M3 - SCORING: Journal article
C2 - 36629274
VL - 51
SP - e20
JO - NUCLEIC ACIDS RES
JF - NUCLEIC ACIDS RES
SN - 0305-1048
IS - 4
ER -
