Description Usage Arguments Details Value Author(s) References See Also Examples

These functions can be run in the parallel mode to reduce the time of
excution. They randomize a network according to the following five
algorithms:

degree preserving randomization (dpr).

similar degree preserving randomization (sdpr).

degree non-preserving randomization (dnpr).

in-degree preserving randomization (idpr).

out-degree preserving randomization (odpr).

1 2 3 4 5 |

`vgraph` |
the graph to be randomized. The graph should be of class igraph. |

`viteration_no` |
an integer scalar indicating the desired number of random graphs to be generated. |

`vparallel` |
a logical scalar indicating whether to use the parallel programming feature to reduce the process time. The default value is FALSE. |

`vcpus` |
an integer scalar determining the number of CPUs to be used when parallel is TRUE. The default value is 1. |

The `dpr()`

function randomizes the input graph by randomly
selecting two of its edges and exchanging their ends. Multiple edges
having the same direction between two nodes are then removed by
switching each of them with randomly selected edges. These steps are
repeated 10 times the number of edges of the input graph. For more
information see references (Abd-Rabbo et al. 2017).

The `sdpr()`

function randomizes the input graph using the
matching algorithm, see references (Milo et al. 2004) and examples.

The `dnpr()`

function randomly selects two nodes with replacement
to create edges.

The `idpr()`

function randomizes the “actor_id” column of
the edges data frame by randomly selecting nodes with replacement from
this column. The edges data frame contains the edges making the network
and contains two columns: “actor_id” and “target_id”.

The `odpr()`

function randomizes the “target_id” column of
the edges data frame by randomly selecting nodes with replacement from
this column. The edges data frame contains the edges making the network
and contains two columns: “actor_id” and “target_id”.

A log file having the name of each of these functions (e.g. dpr_log.txt, idpr_log.txt) will be created in the working directory and could be opened in order to follow the progression of long randomizations. This log file will be deleted at the end of the task.

`graph` |
list of the randomized graphs of class igraph. |

Diala Abd-Rabbo diala.abd.rabbo@gmail.com

Abd-Rabbo, D. and Michnick, S.W. 2017 *BMC Syst Biol* **11**.

Milo, R., Kashtan, N., Itzkovitz, S. 2004 *cond-mat.stat-mech*
arXiv:cond-mat/0312028v2.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ```
## generate a random graph of the kinase-phosphatase network by using
## each of the five algorithms
## load the VertexSort library
library(VertexSort)
## load interactions of the kinase-phosphatase network (kp-net)
data("interactions")
vs_kp_net <- vertex.sort(interactions)
kp_net <- vs_kp_net$graph
## dpr function: randomize a network with preserving its node degrees
## notice the difference in execution time when using and
## not using the parallel programning mode
ptm <- proc.time()
rand_g <- dpr(kp_net, 4) # without parallel mode
proc.time() - ptm
ptm <- proc.time()
rand_g <- dpr(kp_net, 4, TRUE, 4) # with parallel mode
proc.time() - ptm
## verify that rand_g have the same in- and out-degrees as those
## of kp_net should obtain TRUE in both commands.
all(degree(kp_net, V(kp_net), "in")==degree(rand_g[[1]], V(rand_g[[1]]), "in"))
all(degree(kp_net, V(kp_net), "out")==degree(rand_g[[1]], V(rand_g[[1]]), "out"))
## sdpr function: randomize a network with preserving similar node degrees
rand_g <- sdpr(kp_net, 1)
## verify that rand_g have similar in- and out-degrees to those of
## kp_net. Should be -1, 0 or 1
sort(unique(degree(kp_net, V(kp_net), "in")-degree(rand_g[[1]], V(rand_g[[1]]), "in")))
sort(unique(degree(kp_net, V(kp_net), "out")-degree(rand_g[[1]], V(rand_g[[1]]), "out")))
## dnpr function: randomize a network without preserving its node degrees
rand_g <- dnpr(kp_net, 1)
## verify that rand_g have different in- and out-degrees of those of
## kp_net. Should get FALSE in both commands
all(degree(kp_net, V(kp_net), "in")==degree(rand_g[[1]], V(rand_g[[1]]), "in"))
all(degree(kp_net, V(kp_net), "out")==degree(rand_g[[1]], V(rand_g[[1]]), "out"))
## idpr function: randomize a network with preserving its node in-degrees
rand_g <- idpr(kp_net, 1)
## verify that rand_g have same in-degrees and different out-degrees as
## those of kp_net. Should get TRUE and FALSE respectively.
all(degree(kp_net, V(kp_net), "in")==degree(rand_g[[1]], V(rand_g[[1]]), "in"))
all(degree(kp_net, V(kp_net), "out")==degree(rand_g[[1]], V(rand_g[[1]]), "out"))
## odpr function: randomize a network with preserving its node out-degrees
rand_g <- odpr(kp_net, 1)
## verify that rand_g have same out-degrees and different in-degrees as
## those of kp_net. Should get FALSE and TRUE respectively.
all(degree(kp_net, V(kp_net), "in")==degree(rand_g[[1]], V(rand_g[[1]]), "in"))
all(degree(kp_net, V(kp_net), "out")==degree(rand_g[[1]], V(rand_g[[1]]), "out"))
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.