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Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data

Kumar, S. ; Lun, X.-K. ; Bodenmiller, B. ; Rodriguez Martinez, M. ; Koeppl, H. (2020)
Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.
In: Scientific reports, 10
doi: 10.1038/s41598-019-56444-5
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.

Typ des Eintrags: Artikel
Erschienen: 2020
Autor(en): Kumar, S. ; Lun, X.-K. ; Bodenmiller, B. ; Rodriguez Martinez, M. ; Koeppl, H.
Art des Eintrags: Bibliographie
Titel: Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data
Sprache: Englisch
Publikationsjahr: 27 Januar 2020
Verlag: Springer Nature
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Scientific reports
Jahrgang/Volume einer Zeitschrift: 10
DOI: 10.1038/s41598-019-56444-5
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Kurzbeschreibung (Abstract):

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.

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Art.No.: 1233 ; Erstveröffentlichung

Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
Hinterlegungsdatum: 18 Dez 2019 10:34
Letzte Änderung: 03 Jul 2024 02:43
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