# LCC_Significance: LCC Significance In NetSci: Calculates Basic Network Measures Commonly Used in Network Medicine

 LCC_Significance R Documentation

## LCC Significance

### Description

Calculates the Largest Connected Component (LCC) from a given graph, and calculates its significance using a degree preserving approach. Menche, J., et al (2015) <doi.org:10.1126/science.1065103>

### Usage

```LCC_Significance(
N = N,
Targets = Targets,
G,
bins = 100,
hypothesis = "greater",
min_per_bin = 20
)
```

### Arguments

 `N` Number of randomizations. `Targets` Name of the nodes that the subgraph will focus on - Those are the nodes you want to know whether if forms an LCC. `G` The graph of interest (often, in NetMed it is an interactome - PPI). `bins` the number os bins for the degree preserving randomization. When bins = 1, assumes a uniform distribution for nodes. `hypothesis` are you expecting an LCC greater or smaller than the average? `min_per_bin` the minimum size of each bin.

### Value

a list with the LCC - \$LCCZ all values from the randomizations - \$mean the average LCC of the randomizations - \$sd the sd LCC of the randomizations - \$Z The score - \$LCC the LCC of the given targets - \$emp_p the empirical p-value for the LCC - \$rLCC the relative LCC

### 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)

g <- igraph::graph_from_data_frame(net, directed = FALSE )
plot(g)
targets = c("I", "H", "F", "E")
LCC_Significance(N = 100,
Targets = targets,
G = g,
bins = 1,
min_per_bin = 2)

```

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