R/GetMatrixOfCausalRelationships.r

In CausalR: Causal network analysis methods

#' @title compute causal relationships matrix
#' @description 
#' Get a matrix of causal relationships from the network and the IDs of connected nodes
#' @param hypothesis a hypothesis node
#' @param network a CCG network
#' @param idsOfConnectedNodesFromSubgraph a list of connected nodes in the subgraph of interest
#' @return  causal relationships matrix

GetMatrixOfCausalRelationships <- function(hypothesis, network, idsOfConnectedNodesFromSubgraph) {
    
    nNodes <- length(idsOfConnectedNodesFromSubgraph)
    
    causalRelationships <- array(0, nNodes)
    
    for (iNode in 1:nNodes) {
        if (hypothesis == idsOfConnectedNodesFromSubgraph[iNode]) {
            # Hypothesis node's interaction with itself has to be 1.
            causalRelationships[iNode] <- 1
        } else {
            shortestPaths <- igraph::all_shortest_paths(network, hypothesis, idsOfConnectedNodesFromSubgraph[iNode], "out")
            numberOfShortestPaths <- length(shortestPaths[[1]])
            # Determine interaction type for first path
            interactionValue <- DetermineInteractionTypeOfPath(network, shortestPaths[[1]][1])
            
            if (numberOfShortestPaths > 1) {
                # if there is more than one shortest path, two things can happen: 1. they could all agree, in which case we can take any of the values in the vector
                # to populate the data frame 2. they could disagree, in this case the interaction given the value 0 (as opposed to 1/-1 for activation/inhibition.
                
                for (counter in 2:numberOfShortestPaths) {
                    interactionValueForAlternativePath <- DetermineInteractionTypeOfPath(network, shortestPaths[[1]][counter])
                    if (interactionValueForAlternativePath != interactionValue) {
                        interactionValue <- 0
                    (break)()
                    }
                }
            }
            causalRelationships[iNode] <- interactionValue
        }
    }
    
    return(causalRelationships)
}