singleNode.getNodeRanksN {CTD}

Generate single-node node rankings ("fixed" walk)

Description

This function calculates the node rankings starting from a given perturbed variable in a subset of variables in the network.

Usage

singleNode.getNodeRanksN(n,G,p1,thresholdDiff,adj_mat,
                                    S=NULL,num.misses=NULL,verbose=FALSE,
                                    out_dir="",useLabels=FALSE,coords=NULL)

Arguments

n

- The index (out of a vector of node names) of the node ranking you want to calculate.

G

- A list of probabilities with list names being the node names of the network.

p1

- The probability that is preferentially distributed between network nodes by the probability diffusion algorithm based solely on network connectivity. The remaining probability (i.e., "p0") is uniformally distributed between network nodes, regardless of connectivity.

thresholdDiff

- When the probability diffusion algorithm exchanges this amount or less between nodes, the algorithm returns up the call stack.

adj_mat

- The adjacency matrix that encodes the edge weights for the network, G.

S

- A character vector of node names in the subset you want the network walker to find.

num.misses

- The number of "misses" the network walker will tolerate before switching to fixed length codes for remaining nodes to be found.

verbose

- If TRUE, print statements will execute as progress is made. Default is FALSE.

out_dir

- If specified, a image sequence will generate in the output directory specified.

useLabels

- If TRUE, node names will display next to their respective nodes in the network. If FALSE, node names will not display. Only relevant if out_dir is specified.

coords

- The x and y coordinates for each node in the network, to remain static between images.

Value

curr_ns - A character vector of node names in the order they were drawn by the probability diffusion algorithm.

Examples

# Build an adjacency matrix for network G
adj_mat=rbind(c(0,1,2,0,0,0,0,0,0), #A's neighbors
                c(1,0,3,0,0,0,0,0,0), #B's neighbors
                c(2,3,0,0,1,0,0,0,0), #C's neighbors
                c(0,0,0,0,0,0,1,1,0), #D's neighbors
                c(0,0,1,0,0,1,0,0,0), #E's neighbors
                c(0,0,0,0,1,0,0,0,0), #F's neighbors
                c(0,0,0,1,0,0,0,1,0), #G's neighbors
                c(0,0,0,1,0,0,1,0,0), #H's neighbors
                c(0,0,0,0,0,0,0,0,0) #I's neighbors
                )
rownames(adj_mat)=c("A","B","C","D","E","F","G","H","I")
colnames(adj_mat)=c("A","B","C","D","E","F","G","H","I")
G=vector("numeric", length=ncol(adj_mat))
names(G)=colnames(adj_mat)
# Get node rankings for the first metabolite in network G. 
ranks=singleNode.getNodeRanksN(1,G,p1=0.9,thresholdDiff=0.01,adj_mat)