Calculates some indicators for each node in the network.

analyze_network(Omega,nv,...)

Arguments

Omega

a network object

nv

the level of cutoff at which the analysis should be done

label_v : (optionnal) name of the genes

Value

A matrix containing, for each node, its betweenness,its degree, its output, its closeness.

References

Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). Cascade: a R-package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics, btt705.

Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.

Examples

data(network) analyze_network(network,nv=0)
#> Loading required package: tnet
#> Loading required package: igraph
#> #> Attaching package: ‘igraph’
#> The following object is masked from ‘package:Cascade’: #> #> compare
#> The following objects are masked from ‘package:stats’: #> #> decompose, spectrum
#> The following object is masked from ‘package:base’: #> #> union
#> Loading required package: survival
#> tnet: Analysis of Weighted, Two-mode, and Longitudinal networks. #> Type ?tnet for help.
#> node betweenness degree output closeness #> 1 1 0 41 2.115925334 53.79741935 #> 2 2 41 39 2.513381538 49.64857375 #> 3 3 0 32 3.103278157 84.46040797 #> 4 4 0 52 4.837819109 133.68995032 #> 5 5 0 35 1.907441576 53.13521459 #> 6 6 0 35 1.204158962 49.57296793 #> 7 7 0 29 0.491259265 22.02539627 #> 8 8 0 22 0.326333564 67.50590184 #> 9 9 256 37 2.141792871 44.25782447 #> 10 10 137 29 2.173037334 46.65947816 #> 11 11 0 42 2.117408416 105.90041992 #> 12 12 0 46 3.933610474 92.85801386 #> 13 13 0 38 0.540728340 18.54085205 #> 14 14 0 52 1.562246739 52.31739199 #> 15 15 0 42 3.202031415 109.39718660 #> 16 16 281 43 7.707581233 155.29444082 #> 17 17 0 38 3.122945751 78.88975647 #> 18 18 0 44 3.231599597 83.92527638 #> 19 19 0 32 0.522620143 14.85714028 #> 20 20 0 36 0.336035752 20.08353066 #> 21 21 0 3 0.086393474 7.48111170 #> 22 22 0 5 0.065569380 10.29054106 #> 23 23 0 13 0.268899273 9.16543299 #> 24 24 0 2 0.041242098 6.35774369 #> 25 25 0 2 0.037751446 1.34501648 #> 26 26 0 18 0.555031988 20.11288248 #> 27 27 0 42 3.472742236 82.32413143 #> 28 28 0 9 0.550144384 25.36067274 #> 29 29 0 13 0.236333735 7.47709997 #> 30 30 0 7 0.198416275 18.22501773 #> 31 31 0 21 0.549116529 13.74463021 #> 32 32 0 9 0.157283725 10.97812862 #> 33 33 0 11 0.208701671 17.48525682 #> 34 34 0 11 0.521664153 17.87358603 #> 35 35 0 28 2.266892032 52.48268010 #> 36 36 0 1 0.004276882 1.47467352 #> 37 37 0 11 0.430166932 17.93626676 #> 38 38 0 1 0.004096049 0.07445144 #> 39 39 0 6 0.064282146 1.69417015 #> 40 40 0 16 0.462519609 28.27350339 #> 41 41 0 2 0.007001813 0.15809376 #> 42 42 87 20 0.622340946 16.17173024 #> 43 43 0 0 0.000000000 0.00000000 #> 44 44 39 14 0.186791633 5.37542938 #> 45 45 13 4 0.055697064 2.51165809 #> 46 46 0 2 0.007447328 0.13536563 #> 47 47 46 13 0.240128861 6.51734262 #> 48 48 115 17 0.693828497 15.33683233 #> 49 49 51 15 0.378445509 9.29640433 #> 50 50 2 2 0.139848600 2.54194448 #> 51 51 6 2 0.024463423 1.58268047 #> 52 52 0 0 0.000000000 0.00000000 #> 53 53 12 8 0.115966349 2.49617498 #> 54 54 0 0 0.000000000 0.00000000 #> 55 55 0 0 0.000000000 0.00000000 #> 56 56 0 1 0.016258068 0.29551319 #> 57 57 10 4 0.494046644 8.97999078 #> 58 58 0 0 0.000000000 0.00000000 #> 59 59 0 1 0.002284263 0.04151969 #> 60 60 6 3 0.092855604 1.68778085 #> 61 61 6 1 0.113674696 2.06619706 #> 62 62 9 1 0.077398170 1.40682031 #> 63 63 24 3 0.187822345 3.41393459 #> 64 64 0 0 0.000000000 0.00000000 #> 65 65 2 1 0.026431811 0.48043525 #> 66 66 0 0 0.000000000 0.00000000 #> 67 67 0 0 0.000000000 0.00000000 #> 68 68 34 5 0.124270366 2.25878823 #> 69 69 0 0 0.000000000 0.00000000 #> 70 70 0 0 0.000000000 0.00000000 #> 71 71 6 4 0.071416296 1.29809136 #> 72 72 12 2 0.241063855 4.38167372 #> 73 73 0 0 0.000000000 0.00000000 #> 74 74 3 4 0.152181171 2.76610625 #> 75 75 22 2 0.215218316 3.91189479 #> 76 76 7 2 0.066261991 1.20440464 #> 77 77 2 5 0.187968919 3.41659877 #> 78 78 4 2 0.135639956 2.46544647 #> 79 79 30 7 0.556373920 10.11287644 #> 80 80 0 0 0.000000000 0.00000000 #> 81 81 0 0 0.000000000 0.00000000 #> 82 82 30 2 0.612689152 11.13648478 #> 83 83 32 3 0.342675164 6.22860179 #> 84 84 12 4 0.279803534 5.08582172 #> 85 85 11 3 0.182800332 3.32265245 #> 86 86 3 3 0.100248936 1.82216503 #> 87 87 7 2 0.136366661 2.47865534 #> 88 88 0 0 0.000000000 0.00000000 #> 89 89 0 1 0.011706142 0.21277554 #> 90 90 27 4 0.256810158 4.66788487 #> 91 91 5 2 0.052450371 0.95335907 #> 92 92 5 2 0.032030132 0.58219257 #> 93 93 10 2 0.207746410 3.77608241 #> 94 94 5 3 0.103023466 1.87259601 #> 95 95 0 0 0.000000000 0.00000000 #> 96 96 0 0 0.000000000 0.00000000 #> 97 97 1 2 0.011715032 0.21293714 #> 98 98 1 1 0.003691672 0.06710132 #> 99 99 7 3 0.083935163 1.52563932 #> 100 100 0 0 0.000000000 0.00000000 #> 101 101 1 1 0.001458042 0.02650196 #> 102 102 13 6 0.214365366 3.89639123