Description Usage Arguments Value Examples

View source: R/LCC_Significance.R

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>

1 2 3 4 5 6 7 8 | ```
LCC_Significance(
N = N,
Targets = Targets,
G,
bins = 100,
hypothesis = "greater",
min_per_bin = 20
)
``` |

`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. |

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

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
``` |

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