Li, X;
Xie, S;
McColgan, P;
Tabrizi, SJ;
Scahill, R;
Zeng, D;
Wang, Y;
(2018)
Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data.
Frontiers in Genetics
, 9
, Article 430. 10.3389/fgene.2018.00430.
Preview |
Text
LI_fgene-09-00430.pdf - Published Version Download (1MB) | Preview |
Abstract
The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capture essential asymmetric structural information and heterogeneity between DAGs in order to allow for the identification of causal structure with observational data. In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. We propose a penalized likelihood-based approach to handle multi-dimensionality of the DAG model. We propose a fast, iterative computational algorithm, DAG-MM, to estimate parameters in mSEM and achieve desirable sparsity by hard-thresholding the edges. We theoretically prove the identifiability of mSEM. Using simulations and an application to protein signaling data, we show substantially improved performances when compared to existing methods and consistent results with a network estimated from interventional data. Lastly, we identify gray matter atrophy networks in regions of brain from patients with Huntington’s disease and corroborate our findings using white matter connectivity data collected from an independent study.
| Type: | Article |
|---|---|
| Title: | Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fgene.2018.00430 |
| Publisher version: | https://doi.org/10.3389/fgene.2018.00430 |
| Language: | English |
| Additional information: | © 2018 Li, Xie, McColgan, Tabrizi, Scahill, Zeng and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | graphical models, network analysis, causal structure discovery, heterogeneity, regularization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10059558 |
Archive Staff Only
 |
View Item |
