Ridge estimation of network models from time-course omics data

© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim..

Time-course omics experiments enable the reconstruction of the dynamics of the cellular regulatory network. Here, we describe the means for this reconstruction and the downstream exploitation of the inferred network. It is assumed that one of the various vector-autoregressive models (VAR) models presented here serves as a reasonably accurate description of the time-course omics data. The models are estimated through ridge penalized likelihood maximization, accompanied by functionality for the determination of optimal penalty paramaters. Prior knowledge on the network topology is accommodated by the estimation procedures. Various routes that translate the fitted models into more tangible implications for the medical researcher are described. The network is inferred from the-nonsparse-ridge estimates through empirical Bayes probabilistic thresholding. The influence of a (trait of a) molecular entity at the current time on those at future time points is assessed by mutual information, impulse response analysis, and path decomposition of the covariance. The presented methodology is applied to the omics data from the p53 signaling pathway during HPV-induced cellular transformation. All methodology is implemented in the ragt2ridges package, freely available from the Comprehensive R Archive Network.

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:61

Enthalten in:

Biometrical journal. Biometrische Zeitschrift - 61(2019), 2 vom: 01. März, Seite 391-405

Sprache:

Englisch

Beteiligte Personen:

Miok, Viktorian [VerfasserIn]
Wilting, Saskia M [VerfasserIn]
van Wieringen, Wessel N [VerfasserIn]

Links:

Volltext

Themen:

Cervical cancer
Constrained estimation
Journal Article
Maximum likelihood
Mutual information
Path analysis
Penalized estimation
Research Support, Non-U.S. Gov't
Time series analysis
Tumor Suppressor Protein p53
Vector autoregressive process

Anmerkungen:

Date Completed 26.12.2019

Date Revised 26.12.2019

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1002/bimj.201700195

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM287747735