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ℓ 1-Penalized censored Gaussian graphical model.

Authors
  • Augugliaro, Luigi1
  • Abbruzzo, Antonino1
  • Vinciotti, Veronica2
  • 1 Department of Economics, Business and Statistics, University of Palermo, Building 13, Viale delle Scienze, Palermo, Italy. , (Italy)
  • 2 Department of Mathematics, Brunel University London, Kingston Lane, Uxbridge UB8 3PH, UK.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Apr 01, 2020
Volume
21
Issue
2
Identifiers
DOI: 10.1093/biostatistics/kxy043
PMID: 30203001
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithms for inference. We evaluate the computational efficiency of the proposed algorithms by an extensive simulation study and show that, when censored data are available, our proposal is superior to existing competitors both in terms of network recovery and parameter estimation. We apply the proposed method to gene expression data generated by microfluidic Reverse Transcription quantitative Polymerase Chain Reaction technology in order to make inference on the regulatory mechanisms of blood development. A software implementation of our method is available on github (https://github.com/LuigiAugugliaro/cglasso). © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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