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Title: Human face images from multiple perspectives with lighting from multiple directions with no occlusion, glasses and hat
Award ID(s):
1757659
NSF-PAR ID:
10093806
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Data in Brief
Volume:
22
Issue:
C
ISSN:
2352-3409
Page Range / eLocation ID:
522 to 529
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges.

    Results

    In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision.

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    Supplementary information

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