Download Compressed Adjacency Matrices: Untangling Gene Regulatory

infoVis 2012
Kasper Dinkla, Michel A. Westenberg, and Jarke J. van Wijk
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Introduction
Related work
◦ Node-Link Based Representations
◦ ZAME: Interactive Large-Scale Graph Visualization
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TimeMatrix
User Study
Conclusion
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Gene Regulatory Network (GRN)
◦ Low in-degree
 Every gene is regulated by only a few other genes.
Therefore, all nodes of the network have a low indegree.
◦ Scale-free out-degree
 There are few genes that regulate many others, and
many genes that regulate few others. Therefore, the
network’s out-degree distribution follows a power law.
◦ Few cycles
 The network has few cycles because genes rarely
(indirectly) regulate each other both ways.
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node-link diagrams (low edge to node ratio)
◦ high number of intersecting edges
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adjacency matrices (high edge to node ratio)
◦ space-inefficient, sparse network
green: promotion
red: inhibition
orange: both
blue: unspecified
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Compressed Adjacency Matrices (CAM)
◦ Compactness
 This enables a detailed overview of the entire network.
◦ Localization of motifs
 This enables quick detection of subnetworks of
interest.
◦ Consistent arrangement
 This facilitates interaction while preserving visual
orientation.
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the conversion of a network to a CAM is not
trivial and consists of six steps:
1. the network is decomposed into weakly
connected components
2. nodes with identical neighborhoods are grouped
3. strongly connected components are detected
and grouped to form a DAG
4. the nodes of the DAG are partitioned into layers
5. the layers are turned into blocks that form the
backbone of the CAM
6. the blocks are concatenated to form a cascade
from which node positions
directed graph G = (V,E)
V : the set of vertices (nodes)
E : the set of directed edges
between vertices of G
GI = (VI ,EI) of G,
VI : represent non-overlapping subsets of V
with identical neighborhoods
EI: the set of directed edges of GI
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Layers map directly to blocks
◦ a block Bi is derived from its corresponding layer Li
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Hi and Vi, that specify the horizontal and
vertical ordering of Li’s vertices in the CAM
partitioning the vertices of Li into five classes:
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Leaf (PL)
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Short root (PSR)
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Long root (PLR)
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Short hub (PSH)
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Long hub (PLH)
◦ Vertex in Li without successors
◦ Vertex in Li that has a successor but no predecessors and
all successors are leaves in Li+1
◦ Vertex in Li that has a successor but no predecessors and is
not a short root
◦ Vertex in Li that has a predecessor and successor, and all
successors are leaves in Li+1
◦ Vertex in Li that has a predecessor and successor, but is
not a short hub
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categorizing visual analytic tasks in temporal
social network analysis (Tasks 1, 2, and 3),
proposing an adjacency-matrix-based visual
representation (TimeMatrix) for analyzing
temporal graphs that complement node-link
temporal graph visualization techniques, and
supplementing TimeMatrix with interaction
techniques supporting highly interactive
visual exploration of real-world social
networks across multiple levels of analysis.