scholarly journals A weighted network analysis framework for the hourglass effect—And its application in the C. elegans connectome

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0249846
Author(s):  
Ishaan Batta ◽  
Qihang Yao ◽  
Kaeser M. Sabrin ◽  
Constantine Dovrolis

Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.

2021 ◽  
Author(s):  
Ishaan Batta ◽  
Qihang Yao ◽  
Kaeser M. Sabrin ◽  
Constantine Dovrolis

ABSTRACTUnderstanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of theC. elegansand identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.


2011 ◽  
Vol 84 (4) ◽  
Author(s):  
Tiziano Squartini ◽  
Giorgio Fagiolo ◽  
Diego Garlaschelli

Author(s):  
Malith Senaweera ◽  
Ruwanmalee Dissanayake ◽  
Nuwini Chamindi ◽  
Anupa Shyamalal ◽  
Charith Elvitigala ◽  
...  

2009 ◽  
Vol 20 (4) ◽  
pp. 479-514 ◽  
Author(s):  
Giorgio Fagiolo ◽  
Javier Reyes ◽  
Stefano Schiavo

2019 ◽  
Vol 14 (8) ◽  
pp. 762-770
Author(s):  
Mi-Xiao Hou ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Junliang Shang ◽  
Sha-Sha Wu ◽  
...  

Background: As a method to identify Differentially Expressed Genes (DEGs), Non- Negative Matrix Factorization (NMF) has been widely praised in bioinformatics. Although NMF can make DEGs to be easily identified, it cannot provide more associated information for these DEGs. Objective: The methods of network analysis can be used to analyze the correlation of genes, but they caused more data redundancy and great complexity in gene association analysis of high dimensions. Dimensionality reduction is worth considering in this condition. Methods: In this paper, we provide a new framework by combining the merits of two: NMF is applied to select DEGs for dimensionality reduction, and then Weighted Gene Co-Expression Network Analysis (WGCNA) is introduced to cluster on DEGs into similar function modules. The combination of NMF and WGCNA as a novel model accomplishes the analysis of DEGs for cholangiocarcinoma (CHOL). Results: Some hub genes from DEGs are highlighted in the co-expression network. Candidate pathways and genes are also discovered in the most relevant module of CHOL. Conclusion: The experiments indicate that our framework is effective and the works also provide some useful clues to the reaches of CHOL.


NeuroImage ◽  
2010 ◽  
Vol 52 (4) ◽  
pp. 1465-1476 ◽  
Author(s):  
Jeanette A. Mumford ◽  
Steve Horvath ◽  
Michael C. Oldham ◽  
Peter Langfelder ◽  
Daniel H. Geschwind ◽  
...  

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