TY - JOUR
T1 - A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD
AU - Shaddox, Elin
AU - Stingo, Francesco C.
AU - Peterson, Christine B.
AU - Jacobson, Sean
AU - Cruickshank-Quinn, Charmion
AU - Kechris, Katerina
AU - Bowler, Russell
AU - Vannucci, Marina
N1 - Publisher Copyright:
© 2016, International Chinese Statistical Association.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes for four targeted pathways. Furthermore, it identifies gene connections that are disrupted with increased disease severity and that characterize the disease evolution. We also demonstrate the superior performance of our approach with respect to competing methods, using simulated data.
AB - In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes for four targeted pathways. Furthermore, it identifies gene connections that are disrupted with increased disease severity and that characterize the disease evolution. We also demonstrate the superior performance of our approach with respect to competing methods, using simulated data.
KW - Bayesian inference
KW - Chronic obstructive pulmonary disease (COPD)
KW - Gaussian graphical model
KW - Gene network
KW - Markov random field prior
KW - Spike-and-slab prior
UR - http://www.scopus.com/inward/record.url?scp=84992736554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992736554&partnerID=8YFLogxK
U2 - 10.1007/s12561-016-9176-6
DO - 10.1007/s12561-016-9176-6
M3 - Article
C2 - 33912251
AN - SCOPUS:84992736554
SN - 1867-1764
VL - 10
SP - 59
EP - 85
JO - Statistics in Biosciences
JF - Statistics in Biosciences
IS - 1
ER -