avr_proximity_multiple_target_sets | R Documentation |

Calculates the average proximity from a set of targets to a set of source nodes. It is calculate using a degree preserving randomization. It is calculated as described in Guney, E. et al (2016) <doi.org:10.1038/ncomms10331>

avr_proximity_multiple_target_sets( set, G, ST, source, N = 1000, bins = 100, min_per_bin = 20, weighted = FALSE )

`set` |
Name of the sets you have targets for. (In a drug-target setup, those would be the drugs of interest). |

`G` |
The original graph (often an interactome). |

`ST` |
Set-Target data. It is a data.frame with two columns. ID and Target. |

`source` |
The source nodes (disease genes). |

`N` |
Number of randomizations. |

`bins` |
the number os bins for the degree preserving randomization. |

`min_per_bin` |
the minimum size of each bin. |

`weighted` |
consider a weighted graph? TRUE/FALSE |

proximity and its significance based on the degree preserving randomization.

set.seed(666) net = data.frame( Node.1 = sample(LETTERS[1:15], 15, replace = TRUE), Node.2 = sample(LETTERS[1:10], 15, replace = TRUE)) net$value = 1 net = CoDiNA::OrderNames(net) net = unique(net) net$weight = runif(nrow(net)) g <- igraph::graph_from_data_frame(net, directed = FALSE ) S = c("N", "A", "F", "I") T1 = data.frame(ID = "T1", Target = c("H", "M")) T2 = data.frame(ID = "T2", Target = c("G", "O")) avr_proximity_multiple_target_sets(set = c('T1', 'T2'), G = g, source = S, ST = rbind(T1,T2), bins = 1, min_per_bin = 2) # In a weighted graph # avr_proximity_multiple_target_sets(set = c('T1', 'T2'), # G = g, # source = S, # ST = rbind(T1,T2), # bins = 1, # min_per_bin = 2, # weighted = TRUE)

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