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

## 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

 ```1 2 3 4 5 6 7 8``` ```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. `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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```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 = 5, min_per_bin = 2) # in a real interactome, please use the default ```

NetSci documentation built on Dec. 11, 2021, 9:21 a.m.