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genetic_multiplex — Multiplex genetic interactions (2014)

Description

Multiplex networks representing different types of genetic interactions, for different organisms. Layers represent (i) physical, (ii) association, (iii) co-localization, (iv) direct, and (v) suppressive, (vi) additive or synthetic genetic interaction. Edge direction (i,j) indicates gene i interacting with gene j.1


  1. Description obtained from the ICON project. ↩

Tags
Biological Gene regulation Protein interactions Unweighted Multilayer
Citation
  • M. De Domenico, M. A. Porter, & A. Arenas. "MuxViz: a tool for multilayer analysis and visualization of networks". Journal of Complex Networks, cnu038. (2014). 10.1093/comnet/cnu038 Sci-Hub
Upstream URL OK
https://manliodedomenico.com/data.php
Networks
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
Arabidopsis 6,980 18,655 2.67 18.85 21.49 83.73 -0.08 0.03 14 0.96 Directed Unipartite name nodeLabel weight layer 166 KiB 258 KiB 250 KiB 199 KiB
Bos_Multiplex_Genetic 325 322 0.99 2.28 2.56 73.00 -0.24 0.03 12 0.20 Directed Unipartite name nodeLabel weight layer 8 KiB 11 KiB 10 KiB 8 KiB
Candida 367 398 1.08 11.88 3.28 66.85 -0.45 0.00 7 0.83 Directed Unipartite name nodeLabel weight layer 9 KiB 12 KiB 11 KiB 9 KiB
Celegans 3,879 8,182 2.11 13.26 25.60 31.64 -0.14 0.03 13 0.95 Directed Unipartite name nodeLabel weight layer 91 KiB 136 KiB 130 KiB 107 KiB
Drosophila 8,215 43,367 5.28 17.27 46.06 26.69 0.06 0.15 10 0.99 Directed Unipartite name nodeLabel weight layer 239 KiB 415 KiB 388 KiB 311 KiB
DanioRerio 155 188 1.21 2.50 3.63 28.73 0.09 0.16 6 0.20 Directed Unipartite name nodeLabel weight layer 4 KiB 6 KiB 5 KiB 4 KiB
Gallus 313 389 1.24 9.58 5.23 21.46 -0.44 0.01 8 0.65 Directed Unipartite name nodeLabel weight layer 8 KiB 11 KiB 10 KiB 8 KiB
HepatitusCVirus 105 137 1.30 13.10 1.00 0.05 -0.99 0.00 2 0.98 Directed Bipartite name nodeLabel weight layer 3 KiB 4 KiB 4 KiB 3 KiB
Homo 18,222 170,899 9.38 100.44 122.68 14.20 -0.06 0.03 9 1.00 Directed Unipartite name nodeLabel weight layer 607 KiB 1.2 MiB 1.1 MiB 892 KiB
HumanHerpes4 216 259 1.20 11.05 1.60 228.28 -0.52 0.00 7 0.88 Directed Unipartite name nodeLabel weight layer 5 KiB 8 KiB 7 KiB 5 KiB
HumanHIV1 1,005 1,355 1.35 16.35 8.60 26.39 -0.65 0.00 5 1.00 Directed Unipartite name nodeLabel weight layer 23 KiB 34 KiB 32 KiB 25 KiB
Mus 7,747 19,843 2.56 14.41 31.94 59.36 -0.18 0.03 16 0.96 Directed Unipartite name nodeLabel weight layer 183 KiB 296 KiB 282 KiB 230 KiB
Oryctolagus 144 144 1.00 6.52 1.81 0.03 -0.44 0.01 2 0.55 Directed Unipartite name nodeLabel weight layer 4 KiB 6 KiB 5 KiB 4 KiB
Plasmodium 1,203 2,522 2.10 6.05 11.37 12.72 0.01 0.03 10 0.96 Directed Unipartite name nodeLabel weight layer 29 KiB 40 KiB 38 KiB 31 KiB
Rattus 2,640 4,268 1.62 18.11 13.66 85.57 -0.16 0.01 13 0.89 Directed Unipartite name nodeLabel weight layer 63 KiB 92 KiB 87 KiB 70 KiB
Sacchcere 6,570 282,755 43.04 141.24 207.99 4.94 -0.08 0.15 6 1.00 Directed Unipartite name nodeLabel weight layer 520 KiB 1.2 MiB 1.1 MiB 951 KiB
Sacchpomb 4,092 63,677 15.56 64.69 108.20 17.30 -0.23 0.11 8 1.00 Directed Unipartite name nodeLabel weight layer 169 KiB 355 KiB 329 KiB 262 KiB
Xenopus 461 620 1.34 3.43 4.30 115.21 -0.14 0.12 14 0.57 Directed Unipartite name nodeLabel weight layer 11 KiB 17 KiB 15 KiB 12 KiB
YeastLandscape 4,458 8,473,997 1900.85 818.52 3913.37 1.12 -0.01 0.93 2 1.00 Directed Unipartite name nodeLabel weight layer 14.5 MiB 52.4 MiB 35.3 MiB 33.6 MiB
Ridiculograms*
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.