Update getPermsBinary with earlier non-numeric check #72
Comments
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Suggested fix: function (numperms, fg_vec, sisters_list, root_sp, RERmat, trees, |
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Thank you, Michael
This will be a nice addition for users.
-Nathan
On Oct 20, 2022, at 1:18 PM, MichaelTene7 ***@***.******@***.***>> wrote:
Suggested fix:
function (numperms, fg_vec, sisters_list, root_sp, RERmat, trees,
mastertree, permmode = "cc", method = "k", min.pos = 2, trees_list = NULL,
calculateenrich = F, annotlist = NULL)
{
if(!is.numeric(numperms)){
stop("numperms is not a numeric value")
}
pathvec = foreground2Paths(fg_vec, trees, clade = "all",
plotTree = F)
col_labels = colnames(trees$paths)
names(pathvec) = col_labels
if (permmode == "cc") {
print("Running CC permulation")
print("Generating permulated trees")
permulated.binphens = generatePermulatedBinPhen(trees$masterTree,
numperms, trees, root_sp, fg_vec, sisters_list, pathvec,
permmode = "cc")
permulated.fg = mapply(getForegroundsFromBinaryTree,
permulated.binphens[[1]])
permulated.fg.list = as.list(data.frame(permulated.fg))
phenvec.table = mapply(foreground2Paths, permulated.fg.list,
MoreArgs = list(treesObj = trees, clade = "all"))
phenvec.list = lapply(seq_len(ncol(phenvec.table)), function(i) phenvec.table[,
i])
print("Calculating correlations")
corMatList = lapply(phenvec.list, correlateWithBinaryPhenotype,
RERmat = RERmat)
permPvals = data.frame(matrix(ncol = numperms, nrow = nrow(RERmat)))
rownames(permPvals) = rownames(RERmat)
permRhovals = data.frame(matrix(ncol = numperms, nrow = nrow(RERmat)))
rownames(permRhovals) = rownames(RERmat)
permStatvals = data.frame(matrix(ncol = numperms, nrow = nrow(RERmat)))
rownames(permStatvals) = rownames(RERmat)
for (i in 1:length(corMatList)) {
permPvals[, i] = corMatList[[i]]$P
permRhovals[, i] = corMatList[[i]]$Rho
permStatvals[, i] = sign(corMatList[[i]]$Rho) * -log10(corMatList[[i]]$P)
}
}
else if (permmode == "ssm") {
print("Running SSM permulation")
if (is.null(trees_list)) {
trees_list = trees$trees
}
RERmat = RERmat[match(names(trees_list), rownames(RERmat)),
]
print("Generating permulated trees")
permulated.binphens = generatePermulatedBinPhenSSMBatched(trees_list,
numperms, trees, root_sp, fg_vec, sisters_list, pathvec)
df.list = lapply(trees_list, getSpeciesMembershipStats,
masterTree = masterTree, foregrounds = fg_vec)
df.converted = data.frame(matrix(unlist(df.list), nrow = length(df.list),
byrow = T), stringsAsFactors = FALSE)
attr = attributes(df.list[[1]])
col_names = attr$names
attr2 = attributes(df.list)
row_names = attr2$names
colnames(df.converted) = col_names
rownames(df.converted) = row_names
df.converted$num.fg = as.integer(df.converted$num.fg)
df.converted$num.spec = as.integer(df.converted$num.spec)
spec.members = df.converted$spec.members
grouped.trees = groupTrees(spec.members)
ind.unique.trees = grouped.trees$ind.unique.trees
ind.unique.trees = unlist(ind.unique.trees)
ind.tree.groups = grouped.trees$ind.tree.groups
unique.trees = trees_list[ind.unique.trees]
unique.map.list = mapply(matchAllNodesClades, unique.trees,
MoreArgs = list(treesObj = trees))
unique.permulated.binphens = permulated.binphens[ind.unique.trees]
unique.permulated.paths = calculatePermulatedPaths_apply(unique.permulated.binphens,
unique.map.list, trees)
permulated.paths = vector("list", length = length(trees_list))
for (j in 1:length(permulated.paths)) {
permulated.paths[[j]] = vector("list", length = numperms)
}
for (i in 1:length(unique.permulated.paths)) {
ind.unique.tree = ind.unique.trees[i]
ind.tree.group = ind.tree.groups[[i]]
unique.path = unique.permulated.paths[[i]]
for (k in 1:length(ind.tree.group)) {
permulated.paths[[ind.tree.group[k]]] = unique.path
}
}
attributes(permulated.paths)$names = row_names
print("Calculating correlations")
RERmat.list = lapply(seq_len(nrow(RERmat[])), function(i) RERmat[i,
])
corMatList = mapply(calculateCorPermuted, permulated.paths,
RERmat.list)
permPvals = extractCorResults(corMatList, numperms, mode = "P")
rownames(permPvals) = names(trees_list)
permRhovals = extractCorResults(corMatList, numperms,
mode = "Rho")
rownames(permRhovals) = names(trees_list)
permStatvals = sign(permRhovals) * -log10(permPvals)
rownames(permStatvals) = names(trees_list)
}
else {
stop("Invalid binary permulation mode.")
}
if (calculateenrich) {
realFgtree = foreground2TreeClades(fg_vec, sisters_list,
trees, plotTree = F)
realpaths = tree2PathsClades(realFgtree, trees)
realresults = getAllCor(RERmat, realpaths, method = method,
min.pos = min.pos)
realstat = sign(realresults$Rho) * -log10(realresults$P)
names(realstat) = rownames(RERmat)
realenrich = fastwilcoxGMTall(na.omit(realstat), annotlist,
outputGeneVals = F)
groups = length(realenrich)
c = 1
while (c <= groups) {
current = realenrich[[c]]
realenrich[[c]] = current[order(rownames(current)),
]
c = c + 1
}
permenrichP = vector("list", length(realenrich))
permenrichStat = vector("list", length(realenrich))
c = 1
while (c <= length(realenrich)) {
newdf = data.frame(matrix(ncol = numperms, nrow = nrow(realenrich[[c]])))
rownames(newdf) = rownames(realenrich[[c]])
permenrichP[[c]] = newdf
permenrichStat[[c]] = newdf
c = c + 1
}
counter = 1
while (counter <= numperms) {
stat = permStatvals[, counter]
names(stat) = rownames(RERmat)
enrich = fastwilcoxGMTall(na.omit(stat), annotlist,
outputGeneVals = F)
groups = length(enrich)
c = 1
while (c <= groups) {
current = enrich[[c]]
enrich[[c]] = current[order(rownames(current)),
]
enrich[[c]] = enrich[[c]][match(rownames(permenrichP[[c]]),
rownames(enrich[[c]])), ]
permenrichP[[c]][, counter] = enrich[[c]]$pval
permenrichStat[[c]][, counter] = enrich[[c]]$stat
c = c + 1
}
counter = counter + 1
}
}
if (calculateenrich) {
data = vector("list", 5)
data[[1]] = permPvals
data[[2]] = permRhovals
data[[3]] = permStatvals
data[[4]] = permenrichP
data[[5]] = permenrichStat
names(data) = c("corP", "corRho", "corStat", "enrichP",
"enrichStat")
}
else {
data = vector("list", 3)
data[[1]] = permPvals
data[[2]] = permRhovals
data[[3]] = permStatvals
names(data) = c("corP", "corRho", "corStat")
}
data
}
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At the moment, when passing in a non-numeric value of
numpermsthe code will execute the majority of it (90% of run time) and then fail at this line:Error in matrix(ncol = numperms, nrow = nrow(RERmat)) : non-numeric matrix extent Calls: getPermsBinary -> data.frame -> matrixAdding a check earlier to ensure that
numpermsis numeric would be beneficialThe text was updated successfully, but these errors were encountered: