abc {networkABC}

ABC algorithm for network reverse-engineering

Description

ABC algorithm for network reverse-engineering

Usage

abc(
  data,
  clust_coeffs = c(0.33, 0.66, 1),
  tolerance = NA,
  number_hubs = NA,
  iterations = 10,
  number_networks = 1000,
  hub_probs = NA,
  neighbour_probs = NA,
  is_probs = 1
)

Arguments

data

: Any microarray data in the form of a matrix (rows are genes and columns are time points)

clust_coeffs

: one dimensional array of size clust_size of clustering coefficients (these clustering coefficient are tested in the ABc algorithm).

tolerance

: a real value based for the tolerance between the generated networks and the reference network

number_hubs

: number of hubs in the network

iterations

: number of times to repeat ABC algorithm

number_networks

: number of generated networks in each iteration of the ABC algorithm

hub_probs

: one-dimensional array of size number_genes for the each label to be in the role of a hub

neighbour_probs

: this is the matrix of neighbour probabilities of size number_nodes*number_nodes

is_probs

: this needs to be set either to one (if you specify hub_probs and neighbour_probs) or to zero (if neither probabilities are specified). Warning: you should specify both hub_probs and neighbour_probs if is_probs is one. If is_prob is zero these arrays should simply indicate an array of a specified size..

Examples

M<-matrix(rnorm(30),10,3)
result<-abc(data=M)