mle.getPtDist {CTD}

CTDncd: A network-based distance metric.

Description

This function calculates the universal distance between patients, using a mutual information metric, where self-information comes from the minimum encoding length of each patient's encoded modular perturbations in the network.

Usage

mle.getPtDist(p1.optBS,ptID,p2.optBS,ptID2,data_mx,ranks,p1,
                        thresholdDiff,adj_mat)

Arguments

p1.optBS

- The optimal bitstring associated with patient 1.

ptID

- The identifier associated with patient 1's sample.

p2.optBS

- The optimal bitstring associated with patient 2.

ptID2

- The identifier associated with patient 2's sample.

data_mx

- The matrix that gives the perturbation strength (z-scores) for all variables (columns) for each patient (rows).

ranks

- The list of node ranks, starting with each node in patient 1&2's subsets of interest.

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 (thresholdDiff) 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.

Value

patientDistances - a distance matrix, where row and columns are patient identifiers.

Examples

# Get patient distances for the first 2 patients in the Miller 2015 dataset.
data("Miller2015")
data_mx = Miller2015[-c(1,grep("x - ", rownames(Miller2015))),
                        grep("IEM",colnames(Miller2015))]
data_mx = apply(data_mx[,c(1,2)], c(1,2), as.numeric)
# Build a network, G
adj_mat = matrix(0, nrow=nrow(data_mx), ncol=nrow(data_mx))
rows = sample(seq_len(ncol(adj_mat)), 0.1*ncol(adj_mat))
cols = sample(seq_len(ncol(adj_mat)), 0.1*ncol(adj_mat))
for (i in rows) {for (j in cols) {adj_mat[i,j]=rnorm(1,mean=0,sd=1)}}
colnames(adj_mat) = rownames(data_mx)
rownames(adj_mat) = rownames(data_mx)
G = vector("numeric", length=ncol(adj_mat))
names(G)=colnames(adj_mat) 
# Look at the top 5 metabolites for each patient. 
kmx=5
topMets_allpts = c()
for (pt in seq_len(ncol(data_mx))) {
    topMets_allpts=c(topMets_allpts, 
                    rownames(data_mx)[order(abs(data_mx[,pt]),
                                            decreasing=TRUE)[seq_len(kmx)]])}
topMets_allpts = unique(topMets_allpts)
# Pre-compute node ranks for all metabolites in topMets_allpts
# for faster distance calculations.
ranks = list()
for (n in seq_len(length(topMets_allpts))) { 
    ind = which(names(G)==topMets_allpts[n])
    ranks[[n]]=singleNode.getNodeRanksN(ind,G,0.9,0.01,adj_mat,
                                        topMets_allpts,log2(length(G))) 
}
names(ranks) = topMets_allpts
# Also pre-compute patient bitstrings for faster distance calculations.
ptBSbyK = list()
for (pt in seq_len(ncol(data_mx))) {
    S=rownames(data_mx)[order(abs(data_mx[,pt]),
                                decreasing=TRUE)[seq_len(kmx)]]
    ptBSbyK[[pt]] = mle.getPtBSbyK(S, ranks)
}
# Build your results ("res") list object to store patient distances at
# different size k's.
res = list()
t = list(ncd=matrix(NA, nrow=ncol(data_mx), ncol=ncol(data_mx)))
rownames(t$ncd) = colnames(data_mx)
colnames(t$ncd) = colnames(data_mx)
for (i in seq_len(kmx)) { res[[i]] = t }
for (pt in seq_len(ncol(data_mx))) {
    print(pt)
    ptID = colnames(data_mx)[pt]
    for (pt2 in pt:ncol(data_mx)) {
        ptID2 = colnames(data_mx)[pt2]
        tmp=mle.getPtDist(ptBSbyK[[pt]],ptID,ptBSbyK[[pt2]],ptID2,
                            data_mx,ranks,p1=0.9,thresholdDiff=0.01,adj_mat)
        for (k in seq_len(kmx)) {
            res[[k]]$ncd[ptID, ptID2] = tmp$NCD[k]
            res[[k]]$ncd[ptID2, ptID] = tmp$NCD[k]
        }
    }
}