# avr_proximity_multiple_target_sets: avr_proximity_multiple_target_sets In NetSci: Calculates Basic Network Measures Commonly Used in Network Medicine

 avr_proximity_multiple_target_sets R Documentation

## avr_proximity_multiple_target_sets

### Description

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>

### Usage

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

### Arguments

 `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

### Value

proximity and its significance based on the degree preserving randomization.

### Examples

```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)

```

NetSci documentation built on July 4, 2022, 1:05 a.m.