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SigEMDHur.R
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EMDomics <- function(data, outcomes, emd, emd.perm, pairwise.emd.table, pairwise.q.table) {
structure(list("data"=data, "outcomes"=outcomes,
"emd"=emd, "emd.perm"=emd.perm, "pairwise.emd.table"=pairwise.emd.table,
"pairwise.q.table"=pairwise.q.table),
class = "EMDomics")
}
calculate_emd_hur <- function(data, outcomes, binSize=0.2,
nperm=100, pairwise.p=FALSE, seq=FALSE,
quantile.norm=FALSE,
verbose=TRUE, parallel=FALSE) {
bpparam <- BiocParallel::bpparam()
if (parallel == FALSE)
bpparam <- BiocParallel::SerialParam()
if (seq)
{
data<-data*1E6
data<-log2(data+1)
}
if (quantile.norm)
{
preprocessCore::normalize.quantiles(data)
}
# transpose and coerce to df (for bplapply)
data.df <- as.data.frame(t(data))
sample_names <- rownames(data.df)
# ---------- pairwise emd table -----------
# generate pairwise emd table for each gene
if (verbose)
message("Calculating pairwise emd scores...", appendLF=FALSE)
# all possible pairwise comparisons
classes <- unique(outcomes)
pairs <- combn(classes,2)
names <- apply(pairs,2,function(x){paste(x[1],'vs',x[2])})
emd.tab <- BiocParallel::bplapply(data.df, .emd_pairwise_table, sample_names,
outcomes, pairs,
binSize, verbose,
BPPARAM = bpparam)
# Remove genes that were not binned properly by the histogram function
lengths <- lapply(emd.tab, function(x){length(x)})
remove.genes <- lengths < ncol(pairs)
bad.lengths <- lengths[remove.genes]
for (b in names(bad.lengths)) {
msg <- paste('Data for gene', b, 'has been removed because it could not be binned properly.')
message(msg)
}
emd.tab <- emd.tab[!remove.genes]
data.df <- data.df[,!remove.genes]
emd.tab <- matrix(unlist(emd.tab), nrow=ncol(data.df), ncol=ncol(pairs), byrow=TRUE)
rownames(emd.tab) <- colnames(data.df)
colnames(emd.tab) <- names
if (verbose)
message("done.")
# ---------- emd ------------
# calculate emd for each gene
if (verbose)
message("Calculating emd...", appendLF=FALSE)
emd <- apply(emd.tab, 1, function(x){max(as.numeric(x),na.rm=TRUE)})
emd <- as.matrix(emd)
colnames(emd) <- "emd"
if (verbose)
message("done.")
# pairwise q-value computation if specified
if (pairwise.p) {
emd.pairwise.q <- .emd_pairwise_q(data.df, emd, outcomes, nperm, verbose, binSize, bpparam)
} else {
emd.pairwise.q <- NULL
}
# --------------- permuted emd scores ----------------
sample_count <- length(outcomes)
# matrix to hold permuted emd values
emd.perm <- matrix(nrow=ncol(data.df), ncol=nperm)
rownames(emd.perm) <- colnames(data.df)
colnames(emd.perm) <- as.character(1:nperm)
for (i in 1:nperm) {
msg <- paste("Calculating permuted emd #", i, " of ",
nperm, "...", sep="")
if (verbose)
message(msg, appendLF=FALSE)
# permute samples
idx.perm <- sample(1:sample_count, replace=FALSE)
sample.id <- names(outcomes)
outcomes.perm <- outcomes[idx.perm]
names(outcomes.perm) <- sample.id
# calculate emd for permuted samples
perm.val <- BiocParallel::bplapply(data.df, calculate_emd_gene,
outcomes.perm, rownames(data.df),
binSize,
BPPARAM = bpparam)
emd.perm[,i] <- as.numeric(unlist(sapply(perm.val,"[",1)))
if (verbose)
message("done.")
}
# ------------------ q-values --------------------
if (verbose)
message("Calculating q-values...", appendLF=FALSE)
qvals <- matrix(1, nrow=dim(data)[1], ncol=1)
rownames(qvals) <- rownames(emd)
colnames(qvals) <- "qvals "
for (i in 1:dim(data)[1]){
qvals[i,]<- sum(abs(emd.perm[i,])>= abs(as.numeric(emd[i,1])))/(dim(emd.perm)[2])
}
emd.qval <- as.matrix(qvals)
colnames(emd.qval) <- "pvalue"
emd.qval
if (verbose)
message("done.")
emd <- cbind(emd, emd.qval)
EMDomics(data, outcomes, emd, emd.perm, emd.tab, emd.pairwise.q)
}
calculate_emd_gene <- function(vec, outcomes, sample_names, binSize=0.2) {
names(vec) <- sample_names
classes <- unique(outcomes)
pairs <- combn(classes,2)
EMD.tab <- matrix(NA, nrow=1, ncol=dim(pairs)[2])
colnames<-list()
for (p in 1:dim(pairs)[2])
{
inds <- pairs[,p]
src <- inds[1]
sink <- inds[2]
src.lab <- names(outcomes[outcomes==src])
sink.lab <- names(outcomes[outcomes==sink])
EMD <- .emd_gene_pairwise(vec,src.lab,sink.lab,binSize)
EMD.tab[1,p] <- EMD
}
EMD.tab <- as.numeric(EMD.tab)
max(EMD.tab,na.rm=TRUE)
}
.emd_pairwise_q <- function(data.df, emd, outcomes, nperm, verbose, binSize, bpparam) {
classes <- unique(outcomes)
pairs <- combn(classes,2)
names <- apply(pairs,2,function(x){paste(x[1],'vs',x[2])})
q.tab <- matrix(NA, nrow=ncol(data.df), ncol=ncol(pairs))
colnames(q.tab) <- names
rownames(q.tab) <- colnames(data.df)
for (p in 1:ncol(pairs)) {
# ------------------ calculate permuted emd scores ---------------------
sample_count <- length(outcomes)
# matrix to hold permuted emd values
emd.perm <- matrix(nrow=ncol(data.df), ncol=nperm)
rownames(emd.perm) <- colnames(data.df)
colnames(emd.perm) <- as.character(1:nperm)
msg <- paste("Beginning pairwise q-value computatiion for",names[p])
if (verbose)
message(msg)
for (i in 1:nperm) {
msg <- paste("Calculating permuted emd #", i, " of ",
nperm, "...", sep="")
if (verbose)
message(msg, appendLF=FALSE)
# permute samples
idx.perm <- sample(1:sample_count, replace=FALSE)
sample.id <- names(outcomes)
outcomes.perm <- outcomes[idx.perm]
names(outcomes.perm) <- sample.id
# calculate emd for permuted samples
perm.val <- BiocParallel::bplapply(data.df, calculate_emd_gene,
outcomes.perm, rownames(data.df),
binSize,
BPPARAM = bpparam)
emd.perm[,i] <- unlist(sapply(perm.val,"[",1))
if (verbose)
message("done.")
}
# ------------------ q-values --------------------
if (verbose)
message("Calculating pairwise q-values...", appendLF=FALSE)
qvals <- matrix(1, nrow=dim(data)[1], ncol=1)
rownames(qvals) <- rownames(emd)
colnames(qvals) <- "qvals "
for (i in 1:dim(data)[1]){
qvals[i,]<- sum(abs(emd.perm[i,])>= abs(as.numeric(emd[i,1])))/(dim(emd.perm)[2])
}
emd.qval <- as.matrix(qvals)
colnames(emd.qval) <- "pvalue"
emd.qval
q.tab[,p] <- emd.qval
if (verbose)
message("done.")
}
q.tab
}
# Creates a table of all the pairwise EMD scores for one gene
.emd_pairwise_table <- function(geneData, sample_names, outcomes, pairs, binSize, verbose) {
names(geneData) <- sample_names
EMD.tab <- matrix(NA, nrow=1, ncol=dim(pairs)[2])
colnames<-list()
for (p in 1:dim(pairs)[2])
{
inds <- pairs[,p]
src <- inds[1]
sink <- inds[2]
src.lab <- names(outcomes[outcomes==src])
sink.lab <- names(outcomes[outcomes==sink])
EMD <- .emd_gene_pairwise(geneData,src.lab,sink.lab,binSize)
EMD.tab[1,p] <- EMD
}
EMD.tab <- as.numeric(EMD.tab)
EMD.tab
}
# computes pairwise EMD
.emd_gene_pairwise <- function(vec, idxA, idxB, binSize=0.2) {
dataA <- vec[idxA]
dataB <- vec[idxB]
dataA <- as.numeric(dataA)
dataB <- as.numeric(dataB)
bins <- seq(floor(min(c(dataA, dataB))),
ceiling(max(c(dataA, dataB))),
by=binSize )
histA <- hist(dataA, breaks=bins, plot=FALSE)
histB <- hist(dataB, breaks=bins, plot=FALSE)
densA <- as.matrix(histA$density)
densB <- as.matrix(histB$density)
emdist::emd2d(densA, densB)
}