ABC algorithm for network reverse-engineering
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
): Any microarray data in the form of a matrix (rows are genes and columns are time points)
: one dimensional array of size clust_size of clustering coefficients (these clustering coefficient are tested in the ABc algorithm).
: a positive real value based for the tolerance between the generated networks and the reference network
: number of hubs in the network
: number of times to repeat ABC algorithm
: number of generated networks in each iteration of the ABC algorithm
: one-dimensional array of size number_genes for the each label to be in the role of a hub
: this is the matrix of neighbour probabilities of size number_nodes*number_nodes
: 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..
M<-matrix(rnorm(30),10,3)
result<-abc(data=M)
Run the code above in your browser using DataLab