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Mergeomics.R
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#
# Generate input files for Himmeli. The network visualization
# is a streamlined depiction of the module enrichment in hub
# neighborhoods. For instance, only links that connect a key
# driver to another node are depicted.
#
# Input:
# job KDA data list as returned by kda.finish()
#
# Optional input:
# modules array of module names to be visualized
# ndrivers maximum number of drivers per module
#
# Written by Ville-Petteri Makinen 2013
#
kda2himmeli <- function(job, modules=NULL, ndrivers=5) {
# Import node values.
cat("\nImporting node data...\n")
valdata <- tool.read(job$nodfile)
# Set node sizes.
z <- as.double(valdata$VALUE)
z <- (z/quantile(z, 0.95) + rank(z)/length(z))
valdata$SIZE <- pmin(4.0, z)
# Select subset of genes.
valdata <- kda2himmeli.identify(valdata, "NODE", job$graph$nodes)
print(summary(valdata))
# Select top scoring modules.
cat("\nForwarding KDA results to Himmeli...\n")
if(is.null(modules) & (is.null(job$ssearesults) == FALSE)) {
tmp <- job$ssearesults
tmp <- tmp[order(tmp$P),]
modules <- tmp$modules
if(length(modules) > 8) modules <- modules[1:8]
}
# Convert module names to indices.
if(is.null(modules) == FALSE) {
modules <- match(modules, job$modules)
modules <- modules[which(modules > 0)]
if(length(modules) < 1)
stop("Unknown module names.")
}
# Select top key drivers from each module.
drivers <- kda2himmeli.drivers(job$results, modules, ndrivers)
mods <- unique(drivers$MODULE)
palette <- kda2himmeli.colormap(length(mods))
# Process each module separately.
edgdata <- data.frame()
noddata <- data.frame()
for(i in 1:length(mods)) {
rows <- which(drivers$MODULE == mods[i])
tmp <- kda2himmeli.exec(job, valdata, drivers[rows,], mods, palette)
edgdata <- rbind(edgdata, tmp$edat)
noddata <- rbind(noddata, tmp$vdat)
}
# Create work folder.
dpath <- file.path(job$folder, "himmeli")
if(file.exists(dpath) == FALSE)
dir.create(path=dpath, recursive=TRUE)
# Save data files.
edgfile <- file.path(job$folder, "kda2himmeli.edges.txt")
nodfile <- file.path(job$folder, "kda2himmeli.nodes.txt")
tool.save(frame=edgdata, file=edgfile)
tool.save(frame=noddata, file=nodfile)
# Configuration data.
gname <- file.path(dpath, job$label)
instr <- paste("GraphName", gname, sep="\t")
instr <- c(instr, paste("EdgeFile", edgfile, sep="\t"))
instr <- c(instr, paste("EdgeTailVariable", "TAIL", sep="\t"))
instr <- c(instr, paste("EdgeHeadVariable", "HEAD", sep="\t"))
instr <- c(instr, paste("EdgeWeightVariable", "WEIGHT", sep="\t"))
instr <- c(instr, paste("EdgeColorVariable", "COLOR", sep="\t"))
instr <- c(instr, paste("VertexFile", nodfile, sep="\t"))
instr <- c(instr, paste("VertexNameVariable", "NODE", sep="\t"))
instr <- c(instr, paste("VertexColorVariable", "COLOR", sep="\t"))
instr <- c(instr, paste("VertexLabelVariable", "LABEL", sep="\t"))
instr <- c(instr, paste("VertexShapeVariable", "SHAPE", sep="\t"))
instr <- c(instr, paste("VertexSectorVariable", "SECTOR", sep="\t"))
instr <- c(instr, paste("VertexSizeVariable", "SIZE", sep="\t"))
instr <- c(instr, paste("DistanceUnit", "1.1", sep="\t"))
instr <- c(instr, paste("ChassisMode", "on", "2.0", sep="\t"))
# Color info.
modnames <- job$modules[mods]
for(j in 1:ncol(palette)) {
c <- palette[,j]
value <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
value <- paste("VertexColorInfo", modnames[j], value, sep="\t")
instr <- c(instr, value)
}
# Save configuration data.
fname <- file.path(job$folder, "kda2himmeli.config.txt")
write.table(x=instr, sep="\t", file=fname, na="",
row.names=FALSE, quote=FALSE)
cat("\rSaved ", length(instr), " rows in '", fname, "'.\n", sep="")
return(job)
}
#----------------------------------------------------------------------------
kda2himmeli.exec <- function(job, valdata, drivers, modpool, palette) {
# Create star topology.
edgdata <- data.frame()
for(i in unique(drivers$NODE)) {
tmp <- kda2himmeli.edges(job$graph, i, job$depth, job$direction)
edgdata <- rbind(edgdata, tmp)
}
# Select affected nodes.
tmp <- c(edgdata$TAIL, edgdata$HEAD)
pos <- match(valdata$NODE, tmp)
valdata <- valdata[which(pos > 0),]
# Assign node shapes.
valdata$SHAPE <- "circle"
pos <- match(valdata$NODE, drivers$NODE)
valdata[which(pos > 0),"SHAPE"] <- "star"
# Trace module memberships.
noddata <- kda2himmeli.colorize(valdata, job$moddata, modpool, palette)
# Trim edge dataset.
edgdata <- edgdata[which(edgdata$TAIL != edgdata$HEAD),]
edgdata <- unique(edgdata[,c("TAIL", "HEAD", "WEIGHT")])
edgdata$COLOR <- "808080"
# Restore original identities.
edgdata$TAIL <- job$graph$nodes[edgdata$TAIL]
edgdata$HEAD <- job$graph$nodes[edgdata$HEAD]
noddata$NODE <- job$graph$nodes[noddata$NODE]
noddata$LABEL <- noddata$NODE
# Make identities unique for the current module.
modtag <- job$modules[drivers[1,"MODULE"]]
for(i in 1:nrow(noddata))
noddata[i,"NODE"] <- paste(noddata[i,"NODE"], modtag, sep="@")
for(i in 1:nrow(edgdata)) {
edgdata[i,"TAIL"] <- paste(edgdata[i,"TAIL"], modtag, sep="@")
edgdata[i,"HEAD"] <- paste(edgdata[i,"HEAD"], modtag, sep="@")
}
# Return results.
res <- list(edat=edgdata, vdat=noddata)
return(res)
}
#----------------------------------------------------------------------------
kda2himmeli.drivers <- function(data, modules, ndriv) {
nmods <- 8 # optimal number of colors
# Select modules.
if(is.null(modules) == FALSE) {
nmods <- length(unique(modules))
pos <- match(data$MODULE, modules)
data <- data[which(pos > 0),]
if(nrow(data) < 1) stop("No data on target modules.")
}
# Include only significant key drivers.
rows <- which(data$FDR < 0.05)
data <- data[rows,]
if(nrow(data) < 1) stop("No key drivers for target modules.")
# Separate modules.
st <- tool.aggregate(data$MODULE)
blocks <- st$blocks
# Collect top drivers.
peaks <- double()
scores <- data$P
for(k in 1:length(blocks)) {
rows <- blocks[[k]]
peaks[k] <- min(scores[rows])
}
# Collect drivers from top modules.
ind <- integer()
mask <- order(peaks)
mask <- mask[1:min(length(mask),nmods)]
for(k in mask) {
rows <- blocks[[k]]
rows <- rows[order(scores[rows])]
rows <- rows[1:min(length(rows),ndriv)]
ind <- c(ind, rows)
}
return(data[ind,c("MODULE","NODE")])
}
#----------------------------------------------------------------------------
kda2himmeli.edges <- function(graph, center, depth, direction) {
g <- tool.subgraph.search(graph, center, depth, direction)
if(direction >= 0) {
g$HEAD <- g$RANK
g$TAIL <- center
} else {
g$TAIL <- g$RANK
g$HEAD <- center
}
g$WEIGHT <- (g$STRENG)/(1.0 + g$LEVEL)
return(g)
}
#---------------------------------------------------------------------------
kda2himmeli.colorize <- function(noddata, moddata, modpool, palette) {
# Collect module memberships.
pos <- match(moddata$NODE, noddata$NODE)
moddata <- moddata[which(pos > 0),c("MODULE","NODE")]
moddata <- unique(moddata)
# Merge duplicate rows.
st <- tool.aggregate(moddata$NODE)
blocks <- st$blocks
colors <- rep(NA, length(blocks))
sectors <- rep("", length(blocks))
for(k in 1:length(blocks)) {
rows <- blocks[[k]]
mods <- moddata[rows,"MODULE"]
mods <- intersect(mods, modpool)
mods <- sort(unique(mods))
n <- length(mods)
if(n < 1) next
if(n < 2) {
c <- palette[,which(modpool == mods[1])]
colors[k] <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
next;
}
for(i in 1:n) {
c <- palette[,which(modpool == mods[i])]
c <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
if(i < 2) {
sectors[k] <- paste(sectors[k], "1:", c, sep="")
} else {
sectors[k] <- paste(sectors[k], ",1:", c, sep="")
}
}
}
# Combine results.
res <- data.frame(NODE=as.integer(st$labels), COLOR=colors,
SECTOR=sectors, stringsAsFactors=FALSE)
res <- merge(noddata, res, all.x=TRUE)
# Fill in missing values.
rows <- which(is.na(res$COLOR))
res[rows,"COLOR"] <- "909090"
return(res)
}
#----------------------------------------------------------------------------
kda2himmeli.colormap <- function(ncolors) {
palette <- rainbow(n=(ncolors + 2))
palette <- (col2rgb(palette)/255)
# Dampen raw colors.
kappa <- pmax(0.25*palette[1,], 0.25*palette[2,], 0.4*palette[3,])
palette[1,] <- ((1.0 - kappa)*palette[1,] + kappa)
palette[2,] <- ((1.0 - kappa)*palette[2,] + kappa)
palette <- round(99*palette)
# Remove strongest greens (easy to confuse on screen).
while(ncol(palette) > ncolors) {
rb <- (palette[1,] + palette[3,])
palette <- palette[,order(rb)]
ind <- which.max(palette[,2])
mask <- setdiff(1:ncol(palette), ind)
palette <- palette[,mask]
}
# Sort by components.
x <- (10000*palette[1,] + 100*palette[2,] + palette[3,])
return(palette[,order(x)])
}
#----------------------------------------------------------------------------
kda2himmeli.identify <- function(dat, varname, labels) {
if(nrow(dat) < 1) return(dat)
# Find matching identities.
pos <- match(dat[,varname], labels)
rows <- which(pos > 0)
# Select subset.
dat[,varname] <- pos
res <- dat[rows,]
return(res)
}
#
# Generate input files for cytoscape. The network visualization
# is a streamlined depiction of the module enrichment in hub
# neighborhoods. Hence, only links that connect a key
# driver to another node are depicted within a particular depth.
#
# Input:
# job KDA data list as returned by kda.finish()
#
# Optional input:
# modules array of module names to be visualized
# ndrivers maximum number of drivers per module
# depth search depth of subgraphs
#
# Written by Zeyneb Kurt 2015
#
kda2cytoscape <- function(job, node.list=NULL, modules=NULL, ndrivers=5,
depth=1) {
# Select top scoring modules.
cat("\nForwarding KDA results to Cytoscape...\n")
if(is.null(modules) ) # if module names were not provided by user,
# take module names from kda results
modules <- job$modules[unique(job$results$MODULE)]
# Convert module names to indices.
if(!is.null(modules)) {
modules <- match(modules, job$modules)
modules <- modules[which(modules > 0)]
if(length(modules) < 1) stop("Unknown module names.")
}
# Select top key drivers from each module.
drivers <- kda2cytoscape.drivers(job$results, modules, ndrivers)
mods <- unique(drivers$MODULE)
modnames <- job$modules[mods]
modnames[which(mods == 0)] <- "NON.MODULE"
palette <- kda2cytoscape.colormap(length(mods))
palette[,which(mods == 0)] <- c(90,90,90)
if(!is.null(node.list)){
if (all(is.na(match(node.list, job$graph$nodes))) )
stop("These nodes are not in the provided graph")
else{
drivers <- c()
nds <- which(!is.na(match(job$graph$nodes, node.list)))
for(ii in 1:length(nds)){
if (length(which(job$results$NODE == nds[ii])) > 0 ){
mdls <- job$results$MODULE[which(job$results$NODE ==
nds[ii])]
drivers <- rbind(drivers, cbind(mdls, nds[ii]))
}
else
drivers <- rbind(drivers, cbind(0, nds[ii]))
}
}
}
drivers <- as.data.frame(drivers)
colnames(drivers) <- c("MODULE" , "NODE")
# Create work folder.
dpath <- file.path(job$folder, "cytoscape")
if(file.exists(dpath) == FALSE)
dir.create(path=dpath, recursive=TRUE)
# Save top KDAs into file
drivers$MODNAMES <- modnames[match(drivers$MODULE, mods)]
drivers$NODNAMES <- job$graph$nodes[drivers$NODE]
for(i in 1:nrow(drivers))
drivers$COLOR[i] <- sprintf("#%02d%02d%02d",
palette[1, match(drivers$MODULE[i], mods)],
palette[2, match(drivers$MODULE[i], mods)],
palette[3, match(drivers$MODULE[i], mods)])
kdafile <- file.path(dpath, "kda2cytoscape.top.kds.txt")
tool.save(frame=drivers, file=kdafile)
# Process each module separately.
edgdata <- data.frame()
noddata <- data.frame()
for(i in 1:length(mods)) {
rows <- which(drivers$MODULE == mods[i])
if(length(rows) > 0){
tmp <- kda2cytoscape.exec(job, drivers[rows,], mods, palette,
depth)
if (!is.null(tmp)){
edgdata <- rbind(edgdata, tmp$edat)
noddata <- rbind(noddata, tmp$vdat)
}
}
}
noddata[which(noddata[,5] == "") , 5] <- NA
keep.list <- c()
unq.nodes <- unique(noddata$NODE)
for ( i in 1:length(unq.nodes) ) {
rws <- which(noddata[,1] == unq.nodes[i])
if (any(is.na(noddata[rws, 5])) ) {
nn <- which(as.integer(noddata[rws, 3]) == 100)
if (length(nn) >0) keep.list <- c(keep.list, rws[nn[1]])
else keep.list <- c(keep.list, rws[1])
}
else {
keep.list <- c(keep.list, rws[1])
lenlen <- length(strsplit(noddata[rws[1], "SECTOR"], "1:")[[1]])
noddata[rws, "COLOR"] <-
strsplit(noddata[rws[1], "SECTOR"], "1:")[[1]][lenlen]
noddata[rws, "COLOR"] <- paste0("#", noddata[rws[1], "COLOR"])
}
}
noddata <- noddata[keep.list, ]
noddata$MODULE <- NULL
# Save data files.
edgfile <- file.path(dpath, "kda2cytoscape.edges.txt")
nodfile <- file.path(dpath, "kda2cytoscape.nodes.txt")
if (!is.null(edgdata)) tool.save(frame=edgdata, file=edgfile)
if (!is.null(noddata))
tool.save(frame=noddata[, c("NODE", "LABEL", "COLOR", "SIZE",
"SHAPE", "SECTOR", "URL", "LABELSIZE")], file=nodfile)
# Color info.
instr <- NULL
instr <- paste("MODULES", "COLOR", sep="\t")
for(j in 1:ncol(palette)) {
c <- palette[,j]
value <- sprintf("#%02d%02d%02d", c[1], c[2], c[3])
value <- paste(modnames[j], value, sep="\t")
instr <- c(instr, value)
}
fname <- file.path(dpath, "module.color.mapping.txt")
write.table(instr, sep="\t", file=fname, na="", row.names=FALSE,
quote=FALSE, col.names=FALSE)
cat("\rInformation is stored into relevant files.\n", sep="")
return(job)
}
#----------------------------------------------------------------------------
kda2cytoscape.exec <- function(job, drivers, modpool, palette, graph.depth=1){
# Create star topology.
edgdata <- data.frame()
for(j in unique(drivers$NODE)) {
tmp <- kda2cytoscape.edges(job$graph, j, graph.depth, job$direction)
edgdata <- rbind(edgdata, tmp)
}
# Select affected nodes.
tmp <- data.frame(unique(c(edgdata$TAIL, edgdata$HEAD)),
stringsAsFactors=FALSE)
names(tmp) <- "NODE"
# Assign node shapes.
shapes <- rep("Ellipse", length(tmp))
sizes <- rep(50, length(tmp))
tmp$SHAPE <- shapes
tmp$SIZE <- sizes
pos <- match(tmp$NODE, drivers$NODE)
tmp[which(pos > 0),"SHAPE"] <- "Diamond"
tmp[which(pos > 0),"SIZE"] <- 100
# Trace module memberships.
noddata <- kda2cytoscape.colorize(tmp, job$moddata, modpool, palette)
if(!is.null(noddata)){
# Trim edge dataset.
edgdata <- edgdata[which(edgdata$TAIL != edgdata$HEAD),]
edgdata <- unique(edgdata[,c("TAIL", "HEAD", "WEIGHT")])
edgdata$COLOR <- "#808080"
# Restore original identities.
edgdata$TAIL <- job$graph$nodes[edgdata$TAIL]
edgdata$HEAD <- job$graph$nodes[edgdata$HEAD]
modtag <- job$modules[drivers[1,"MODULE"]]
if(length(modtag) == 0) modtag <- "NON.MODULE"
noddata$NODE <- job$graph$nodes[as.numeric(noddata$NODE)]
noddata$LABEL <- noddata$NODE
# Make identities unique for the current module.
noddata[1:nrow(noddata), "MODULE"] <- modtag
edgdata[1:nrow(edgdata), "MODULE"] <- modtag
# Return results.
res <- list(edat=edgdata, vdat=noddata)
return(res)
}
else res=NULL
return(res)
}
#----------------------------------------------------------------------------
kda2cytoscape.drivers <- function(data, modules, ndriv) {
nmods <- length(unique(data$MODULE))
# Select modules.
if(!is.null(modules)) {
nmods <- length(unique(modules))
pos <- match(data$MODULE, modules)
data <- data[which(pos > 0),]
if(nrow(data) < 1) stop("No data on target modules.")
}
# Include only significant key drivers.
rows <- which(data$FDR < 0.05)
data <- data[rows,]
if(nrow(data) < 1) stop("No key drivers for target modules.")
# Separate modules.
st <- tool.aggregate(data$MODULE)
blocks <- st$blocks
# Collect top drivers.
peaks <- double()
scores <- data$P
for(k in 1:length(blocks)) {
rows <- blocks[[k]]
peaks[k] <- min(scores[rows])
}
# Collect drivers from modules.
ind <- integer()
mask <- order(peaks)
mask <- mask[1:min(length(mask),nmods)]
for(k in mask) {
rows <- blocks[[k]]
rows <- rows[order(scores[rows])]
rows <- rows[1:min(length(rows),ndriv)]
ind <- c(ind, rows)
}
return(data[ind,c("MODULE","NODE")])
}
#----------------------------------------------------------------------------
kda2cytoscape.edges <- function(graph, center, depth, direction) {
global.HEAD <- c()
global.TAIL <- c()
centeri <- center
for(i in 1:depth){
new.cent <- c()
for(j in centeri){
gi <- tool.subgraph.search(graph, j, depth=1, direction)
if(direction >= 0) {
global.HEAD <- c(global.HEAD, gi$RANK)
global.TAIL <- c(global.TAIL, rep(j, length(gi$RANK)) )
} else {
global.TAIL <- c(global.TAIL, gi$RANK)
global.HEAD <- c(global.HEAD, rep(j, length(gi$RANK)) )
}
new.cent <- unique(c(new.cent, gi$RANK))
}
centeri <- new.cent[which(!(new.cent %in% centeri))]
}
g <- unique(data.frame(cbind(HEAD=global.HEAD, TAIL=global.TAIL,
WEIGHT=1)))
return(g)
}
#---------------------------------------------------------------------------
kda2cytoscape.colorize <- function(noddata, moddata, modpool, palette) {
# Google chart service.
urlbase <- "http://chart.apis.google.com/chart?cht=p&chs=200x200"
urlbase <- paste(urlbase, "chf=bg,s,00000000", sep="&")
# Collect module memberships.
pos <- match(moddata$NODE, noddata$NODE)
moddata <- moddata[which(pos > 0),c("MODULE","NODE")]
moddata <- unique(moddata)
# moddata <- moddata[which(!is.na(match(moddata$MODULE, modpool))), ]
# Merge duplicate rows.
if(length(moddata$NODE) > 0){
st <- tool.aggregate(moddata$NODE)
blocks <- st$blocks
colors <- rep(NA, length(blocks))
sectors <- rep("", length(blocks))
urls <- rep("", length(blocks))
for(k in 1:length(blocks)) {
chd <- ""
chco <- ""
rows <- blocks[[k]]
mods <- moddata[rows,"MODULE"]
mods <- intersect(mods, modpool)
mods <- sort(unique(mods))
n <- length(mods)
if(n < 1) next
for(i in 1:n) {
c <- palette[,which(modpool == mods[i])]
c <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
if(i < 2) {
chd <- "chd=t:1"
chco <- paste("chco=", c, sep="")
cc <- paste("#", c, sep="")
colors[k] <- cc
sectors[k] <- paste(sectors[k], "1:", c, sep="")
} else {
chd <- paste(chd, 1, sep=",")
chco <- paste(chco, c, sep="|")
sectors[k] <- paste(sectors[k], ",1:", c, sep="")
}
}
urls[k] <- paste(urlbase, chd, chco, sep="&")
}
label.size <- rep(12, length(blocks))
# keep 1st clr in colors, keep all sectors in sectors
res <- data.frame(NODE=as.integer(st$labels), COLOR=colors,
SECTOR=sectors, URL=urls, LABELSIZE=label.size,
stringsAsFactors=FALSE)
res <- merge(noddata, res, all.x=TRUE)
# Fill in missing values.
rows <- which(is.na(res$COLOR))
res[rows,"COLOR"] <- "#909090"
res[which(as.integer(res$SIZE) == 100), "LABELSIZE"] <- 20
return(res)
}
else res <- NULL
return (res)
}
#----------------------------------------------------------------------------
kda2cytoscape.colormap <- function(ncolors) {
palette <- rainbow(n=(ncolors + 2))
palette <- (col2rgb(palette)/255)
# Dampen raw colors.
kappa <- pmax(0.25*palette[1,], 0.25*palette[2,], 0.4*palette[3,])
palette[1,] <- ((1.0 - kappa)*palette[1,] + kappa)
palette[2,] <- ((1.0 - kappa)*palette[2,] + kappa)
palette <- round(99*palette)
# Remove strongest greens (easy to confuse on screen).
if (ncolors == 1){
rb <- (palette[1,] + palette[3,])
palette <- palette[,order(rb)]
ind <- which.max(palette[,2])
mask <- setdiff(1:ncol(palette), ind)
palette <- palette[,mask]
}
else {
while(ncol(palette) > ncolors) {
rb <- (palette[1,] + palette[3,])
palette <- palette[,order(rb)]
ind <- which.max(palette[,2])
mask <- setdiff(1:ncol(palette), ind)
palette <- palette[,mask]
}
}
# Sort by components.
x <- (10000*palette[1,] + 100*palette[2,] + palette[3,])
return(palette[,order(x)])
}
#----------------------------------------------------------------------------
kda2cytoscape.identify <- function(dat, varname, labels) {
if(nrow(dat) < 1) return(dat)
# Find matching identities.
pos <- match(dat[,varname], labels)
rows <- which(pos > 0)
# Select subset.
dat[,varname] <- pos
res <- dat[rows,]
return(res)
}
# Determine statistical significance of key driver genes.
#
# Input:
# job$graph see tool.graph()
# job$graph$hubs nodes considered hubs (indexed)
# job$graph$hubnets lists of neighboring nodes (indexed)
# job$graph$cohubsets lists of overlapping hubs (indexed)
# job$module2nodes lists of node indices for each module
#
# Output:
# job$results data frame of results (indexed)
#
# Written by Ville-Petteri Makinen 2013
#
kda.analyze <- function(job) {
set.seed(job$seed)
cat("\nAnalyzing network...\n")
nmods <- length(job$modules)
# Analyze modules.
res <- data.frame()
hubs <- job$graph$hubs
for(i in 1:nmods) {
members <- job$module2nodes[[i]]
p <- kda.analyze.exec(members, job$graph, job$nperm)
mask <- which(p >= 0.0)
if(length(mask) < 1) next
# Find top hit.
tmp <- data.frame(MODULE=i, NODE=hubs[mask], P=p[mask])
pmin <- min(tmp$P)
hit <- which(tmp$P == pmin)
hit <- tmp$NODE[hit[1]]
# Update results.
nmemb <- length(members)
name <- job$graph$nodes[hit]
kd <- sprintf("%s, n=%d, p=%.2e", name, nmemb, pmin)
cat(job$modules[i], ": ", kd, "\n", sep="")
res <- rbind(res, tmp)
}
# Estimate false discovery rates.
res$FDR <- p.adjust(res$P, method="fdr")
job$results <- res
return(job)
}
#----------------------------------------------------------------------------
kda.analyze.exec <- function(memb, graph, nsim) {
hubs <- graph$hubs
hubnets <- graph$hubnets
nhubs <- length(hubs)
nnodes <- length(graph$nodes)
nmemb <- length(memb)
# Observed enrichment scores.
obs <- rep(NA, nhubs)
for(k in 1:nhubs) {
g <- hubnets[[hubs[k]]]
obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)
}
# Estimate P-values.
pvals <- rep(NA, nhubs)
for(k in which(obs > 0)) {
g <- hubnets[[hubs[k]]]
# First pass.
x <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, 200)
# Estimate preliminary P-value.
param <- tool.normalize(x)
z <- tool.normalize(obs[k], param)
p <- pnorm(z, lower.tail=FALSE)
if(p*nhubs > 2.0) next
# Estimate final P-value.
n <- min(nsim, abs(nsim - length(x))+1) # it was: (nsim - length(x))
y <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, n)
param <- tool.normalize(c(x, y))
z <- tool.normalize(obs[k], param)
p <- pnorm(z, lower.tail=FALSE)
p <- max(p, .Machine$double.xmin)
if(p*nhubs > 1.0) next
# Apply Bonferroni adjustment.
pvals[k] <- p*nhubs
}
return(pvals)
}
#----------------------------------------------------------------------------
kda.analyze.simulate <- function(o, g, nmemb, nnodes, nsim) {
neigh <- as.integer(g$RANK)
w <- as.double(g$STRENG)
# Simulate null distribution.
nfalse <- 0
x <- rep(NA, nsim)
for(n in 1:nsim) {
if(nfalse > 10) break
memb <- sample.int(nnodes, nmemb)
x[n] <- kda.analyze.test(neigh, w, memb, nnodes)
if(is.na(x[n])) x[n] <- rnorm(1)
nfalse <- (nfalse + as.integer(x[n] >= o))
}
# Trim results.
x <- x[which(0*x == 0)]
return(x)
}
#----------------------------------------------------------------------------
#kda.analyze.test <- function(ind, w, members, nnodes) {
# shared <- which(match(ind, members) > 0)
# obsmass <- sum(w[shared])
# return(obsmass)
#}
#----------------------------------------------------------------------------
kda.analyze.test <- function(neigh, w, members, nnodes) {
# Check if enough neighbors.
nneigh <- length(neigh)
if(nneigh < 3) return(0.0)
# Find member nodes.
nmemb <- length(members)
pos <- match(neigh, members)
shared <- which(pos > 0)
# Background edge mass.
totmass <- sum(w)
# Edge mass captured by members.
obsmass <- sum(w[shared])
# Effective number of shared nodes.
rho <- (obsmass/totmass)
nobserv <- rho*nneigh
# Expected number of shared nodes.
nexpect <- (nmemb/nnodes)*nneigh
# Calculate enrichment score.
if(nobserv < 1.0)
return(NA)
z <- (nobserv - nexpect)/(sqrt(nexpect) + 1.0)
return(z)
}
#
# Set parameters for a weighted key driver analysis
#
# Input:
# plan$label unique identifier for the analysis
# plan$folder parent folder for results
# plan$netfile path to network file
# columns: TAIL HEAD WEIGHT
# plan$modfile path to module file
# columns: MODULE NODE
#
# Optional:
# plan$inffile path to module info file
# columns: MODULE DESCR
# plan$nodfile path to node selection file
# columns: NODE
# plan$depth search depth for subgraph search
# plan$direction use zero for undirected, negative for
# downstream and positive for upstream
# plan$maxoverlap maximum allowed overlap between two
# key driver neighborhoods
# plan$minsize minimum module size
# plan$mindegree minimum node degree to qualify as a hub
# plan$maxdegree maximum node degree to include
# plan$edgefactor influence of node strengths:
# 0.0 no influence, 1.0 full influence
# plan$seed seed for random number generator
#
# Output:
# job data structure for KDA
#
# Written by Ville-Petteri Makinen 2013
#
kda.configure <- function(plan) {
cat("\nKDA Version:12.7.2015\n")
cat("\nParameters:\n")
plan$stamp <- Sys.time()
if(is.null(plan$folder)) stop("No parent folder.")
if(is.null(plan$label)) stop("No job label.")
if(is.null(plan$netfile)) stop("No network file.")
if(is.null(plan$modfile)) stop("No module file.")
if(is.null(plan$depth)) plan$depth <- 1
plan$depth <- round(plan$depth)
if(plan$depth < 1) stop("Unusable search depth.")
cat(" Search depth: ", plan$depth, "\n", sep="")
if(is.null(plan$direction)) plan$direction <- 0
plan$direction <- round(plan$direction)
if(abs(plan$direction) > 1) stop("Unusable search direction.")
cat(" Search direction: ", plan$direction, "\n", sep="")
if(is.null(plan$maxoverlap)) plan$maxoverlap <- 0.33
if(plan$maxoverlap > 1) stop("Unusable overlap limit.")
if(plan$maxoverlap < 0) stop("Unusable overlap limit.")
cat(" Maximum overlap: ", plan$maxoverlap, "\n", sep="")
if(is.null(plan$minsize)) plan$minsize <- 20
if(plan$minsize < 1) stop("Unusable size limit.")
cat(" Minimum module size: ", plan$minsize, "\n", sep="")
if(is.null(plan$mindegree)) plan$mindegree <- "automatic"
if(is.null(plan$maxdegree)) plan$maxdegree <- "automatic"
cat(" Minimum degree: ", plan$mindegree, "\n", sep="")
cat(" Maximum degree: ", plan$maxdegree, "\n", sep="")
if(is.null(plan$edgefactor)) plan$edgefactor <- 0.5
if(plan$edgefactor > 1) stop("Unusable edge factor.")
if(plan$edgefactor < 0) stop("Unusable edge factor.")
cat(" Edge factor: ", plan$edgefactor, "\n", sep="")
if(is.null(plan$nperm)) plan$nperm <- 2000
if(is.null(plan$seed)) plan$seed <- 1
cat(" Random seed: ", plan$seed, "\n", sep="")
return(plan)
}
# Organize and save results.
#
# Input:
# job$label unique identifier for the analysis
# job$folder output folder for results
#
# Results are also saved in tab-delimited text files.
#
# Written by Ville-Petteri Makinen 2013
#
kda.finish <- function(job) {
cat("\nFinishing results...\n")
if (nrow(job$results)==0){
cat("No Key Driver Found!!!!")
}else{
# Estimate additional measures.
res <- kda.finish.estimate(job)
# Save full results.
res <- kda.finish.save(res, job)
# Create a simpler file for viewing.
res <- kda.finish.trim(res, job)
# Create a summary file of top hits.
res <- kda.finish.summarize(res, job)
return(job)
}
}
#---------------------------------------------------------------------------
kda.finish.estimate <- function(job) {
res <- job$results
# Collect module sizes.
sizes <- rep(0, nrow(res))
modnames <- res$MODULE
mod2nod <- job$module2nodes
for(i in 1:nrow(res)) {
key <- modnames[i]
sizes[i] <- length(mod2nod[[key]])
}
res$N.mod <- sizes
# Collect overlaps with hub neighborhoods.
nnodes <- length(job$graph$nodes)
hubnets <- job$graph$hubnets
sizes <- matrix(nrow=nrow(res), ncol=4)
nodenames <- res$NODE
for(i in 1:nrow(res)) {
key <- modnames[i]
node <- nodenames[i]
memb <- mod2nod[[key]]
g <- hubnets[[node]]
sizes[i,1] <- nrow(g)
sizes[i,2] <- length(intersect(g$RANK, memb))
sizes[i,3] <- nrow(g)*length(memb)/nnodes
sizes[i,4] <- sum(node == memb)
}
# Update data frame.
res$N.neigh <- sizes[,1]
res$N.obsrv <- sizes[,2]
res$N.expct <- sizes[,3]
res$MEMBER <- (sizes[,4] > 0)
res$FILL <- (res$N.obsrv)/(res$N.neigh + 1e-20)
res$FOLD <- (res$N.obsrv)/(res$N.expct + 1e-20)
return(res)
}
#---------------------------------------------------------------------------
kda.finish.save <- function(res, job) {
# Collect co-hubs.
mtx <- matrix(nrow=0, ncol=0)
nodes <- job$graph$nodes
masters <- unique(res$NODE)
cohubsets <- job$graph$cohubsets
for(key in masters) {