#' Prize-collecting Steiner Forest (PCSF) with randomized edge costs
#'
#' \code{PCSF_rand} returns a union of subnetworks obtained by solving the PCSF on the
#' given interaction network by adding a random noise to edge costs each time.
#' @param ppi An interaction network as an \pkg{igraph} object.
#' @param terminals A list of terminal genes with prizes to be analyzed in the PCSF context.
#' A named \code{numeric} vector, where terminal genes are named same as in the interaction network
#' and numeric values correspond to the importance of the gene within the study.
#' @param n An \code{integer} value to determine the number of runs with random noise added edge costs.
#' A default value is 10.
#' @param r A \code{numeric} value to determine additional random noise to edge costs.
#' A random noise upto r percent of the edge cost is added to each edge. A default value is 0.1
#' @param w A \code{numeric} value for tuning the number of trees in the output. A default value is 2.
#' @param b A \code{numeric} value for tuning the node prizes. A default value is 1.
#' @param mu A \code{numeric} value for a hub penalization. A default value is 0.0005.
#' @return The final subnetwork obtained by taking the union of the PCSF outputs generated by
#' adding a random noise to edge costs each time. It returns an \pkg{igraph} object with the node prize
#' and edge cost attributes representing the total number of show ups throughout all runs.
#' @import igraph
#' @export
#'
#'
#' @details
#'
#' In order to increase the robustness of the resulting structure,
#' it is recommended to solve the PCSF several times on the same network
#' while adding some noise to the edge costs each time, and combine all results
#' in a final subnetwork. The union of all outputs may explain
#' the underlying biology better.
#'
#' @examples
#' \dontrun{
#' library("PCSF")
#' data("STRING")
#' data("Tgfb_phospho")
#' terminals <- Tgfb_phospho
#' ppi <- construct_interactome(STRING)
#' subnet <- PCSF_rand(ppi, terminals, n = 10, r =0.1, w = 2, b = 2, mu = 0.0005)}
#'
#' @author Murodzhon Akhmedov
#'
#' @seealso \code{\link{PCSF}}, \code{\link{plot.PCSFe}}
#'
#' @references
#' Akhmedov M., LeNail A., Bertoni F., Kwee I., Fraenkel E., and Montemanni R. (2017)
#' A Fast Prize-Collecting Steiner Forest Algorithm for Functional Analyses in Biological Networks.
#' \emph{Lecture Notes in Computer Science}, to appear.
PCSF_rand <-
function(ppi, terminals, n = 10, r = 0.1, w = 2, b = 1, mu = 0.0005){
# Checking function arguments
if (missing(ppi))
stop("Need to specify an interaction network \"ppi\".")
if (class(ppi) != "igraph")
stop("The interaction network \"ppi\" must be an igraph object.")
if (missing(terminals))
stop(" Need to provide terminal nodes as a named numeric vector,
where node names must be same as in the interaction network.")
if(is.null(names(terminals)))
stop(" The terminal nodes must be provided as a named numeric vector,
where node names must be same as in the interaction network.")
# Gather the terminal genes to be analyzed, and their scores
terminal_names = names(terminals)
terminal_values = as.numeric(terminals)
# Incorporate the node prizes
node_names = V(ppi)$name
node_prz = vector(mode = "numeric", length = length(node_names))
index = match(terminal_names, node_names)
percent = signif((length(index) - sum(is.na(index)))/length(index)*100, 4)
if (percent < 5)
stop(" Less than 1% of your terminal nodes are matched in the interactome, check your terminals!")
cat(paste0(" ", percent, "% of your terminal nodes are included in the interactome\n"))
terminal_names = terminal_names[!is.na(index)]
terminal_values = terminal_values[!is.na(index)]
index = index[!is.na(index)]
node_prz[index] = terminal_values
## Prepare input file for MST-PCSF implementation in C++
cat(" Solving the PCSF by adding random noise to the edge costs...\n")
# Calculate the hub penalization scores
node_degrees = igraph::degree(ppi)
hub_penalization = - mu*node_degrees
# Update the node prizes
node_prizes = b*node_prz
index = which(node_prizes==0)
node_prizes[index] = hub_penalization[index]
# Construct the list of edges
edges = ends(ppi,es = E(ppi))
from = c(rep("DUMMY", length(terminal_names)), edges[,1])
to = c(terminal_names, edges[,2])
all_nodes=NULL
# Run the MST-PCSF algorithm for n times with random noise added to edge costs at each time
for(i in 1:n){
# Randomize the edge costs
cost = c(rep(w, length(terminal_names)), E(ppi)$weight + E(ppi)$weight*stats::runif(length(E(ppi)), 0, r))
# Feed in the input files into MSt-PCSF algorithm
output = call_sr(from,to,cost,node_names,node_prizes)
# Construct an igraph object for current parameter set, and save it
edge = data.frame(as.character(output[[1]]),as.character(output[[2]]))
colnames(edge)= c("source", "target")
edge = edge[which(edge[,1]!="DUMMY"), ]
edge = edge[which(edge[,2]!="DUMMY"), ]
graph = graph.data.frame(edge,directed=F)
assign(paste0("graph_",i), get("graph"))
all_nodes = c(all_nodes, V(graph)$name)
}
# Calculate graph statistics
node_frequency = table(all_nodes)
node_names = names(node_frequency)
node_prizes = as.numeric(node_frequency)
# Combine the graphs in order to get unionized graph
adj_matrix = matrix(0, length(node_names), length(node_names))
colnames(adj_matrix) = node_names
rownames(adj_matrix) = node_names
for(i in 1:n){
assign("graph", get(paste0("graph_",i)))
edges = ends(graph,es = E(graph))
x = match(edges[,1],node_names)
y = match(edges[,2],node_names)
if(length(x)>0 & length(y)>0){
for( j in 1:length(x)){
if(x[j]>=y[j]){
k = x[j]
l = y[j]
}else{
k = y[j]
l = x[j]
}
adj_matrix[k,l] = adj_matrix[k,l]+1
}
}
}
# Check the size of output subnetwork and print a warning if it is 0
if(sum(adj_matrix) != 0){
# Construct the igraph object from the union graph
subnet = graph_from_adjacency_matrix(adj_matrix, weighted=TRUE, mode="undirected")
index = match(V(subnet)$name, node_names)
V(subnet)$prize = node_prizes[index]
# # Associate the type of nodes to shape
# V(subnet)$shape = "triangle"
# index = match(terminal_names, V(subnet)$name)
# index = index[!is.na(index)]
# V(subnet)$shape[index] = "circle"
# Associate the type of nodes
V(subnet)$type = "Steiner"
index = match(terminal_names, V(subnet)$name)
index = index[!is.na(index)]
V(subnet)$type[index] = "Terminal"
class(subnet) <- c("PCSF", "igraph")
return (subnet)
} else {
stop(" Subnetwork can not be identified for a given parameter set.
Provide a compatible b or mu value with your terminal prize list...\n\n")
}
}
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