mle.getMinPtDistance {CTD}R Documentation

Get minimum patient distances

Description

Given a series of patient distance matrices, return the minimum distance between all pairwise patient comparisons made.

Usage

mle.getMinPtDistance(allSimMatrices)

Arguments

allSimMatrices

- A list of all similarity matrices, across all k for a given graph, or across many graphs.

Value

minPtSim - Pairwise patient distances representing the minimum patient distance observed across several distance matrices.

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(1,0,1)}}
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]
        }
    }
}
res_ncd = lapply(res, function(i) i$ncd)
minPtDist = mle.getMinPtDistance(res_ncd)

[Package CTD version 1.0.0 Index]