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# Load a network in a specified rule description language
# from file <file>.
# <bodySeparator> is the character that separates targets and factors
# <lowercaseGenes> specifies whether gene names are converted to lower case
loadNetwork <- function(file, bodySeparator=",", lowercaseGenes=FALSE, symbolic=FALSE)
{
func <- readLines(file,-1)
# strip comments
func <- gsub("#.*", "", trim(func))
func <- func[nchar(func) > 0]
# check header
if (length(func) == 0)
stop("Header expected!")
header <- func[1]
header <- tolower(trim(strsplit(header, bodySeparator)[[1]]))
if (length(header) < 2 || header[1] != "targets" || !(header[2] %in% c("functions","factors")) ||
(length(header) == 3 && header[3] != "probabilities"))
stop(paste("Invalid header:", func[1]))
func <- func[-1]
if (lowercaseGenes)
func <- tolower(func)
# Replace all invalid characters to be able to load most networks
func <- gsub("[^\\[\\]a-zA-Z0-9_\\|\\&!\\(\\) \t\\-+=.,]+","_", func, perl=TRUE)
tmp <- unname(lapply(func,function(x)
# split strings at separators that are NOT
# inside a bracket block
{
bracketCount <- 0
lastIdx <- 1
chars <- strsplit(x,split="")[[1]]
res <- c()
if (length(chars) > 0)
{
for (i in seq_along(chars))
{
if (chars[i] == "(")
bracketCount <- bracketCount + 1
else
if (chars[i] == ")")
bracketCount <- bracketCount -1
else
if (chars[i] == bodySeparator && bracketCount == 0)
{
res <- c(res, trim(paste(chars[lastIdx:(i-1)],collapse="")))
lastIdx <- i + 1
}
}
res <- c(res, trim(paste(chars[lastIdx:length(chars)],collapse="")))
}
return(res)
}))
targets <- sapply(tmp,function(rule)trim(rule[1]))
for (target in targets)
{
if (regexec("^[a-zA-Z_][a-zA-Z0-9_]*$", target)[[1]] == -1)
stop(paste("Invalid gene name:",target))
}
factors <- sapply(tmp,function(rule)trim(rule[2]))
temporal <- length(grep("timeis|timelt|timegt|\\[|\\]", factors, ignore.case=TRUE) > 0)
if (temporal && !symbolic)
{
warning("The network contains temporal elements. This requires loading the model with symbolic=TRUE!")
symbolic <- TRUE
}
probabilities <- sapply(tmp,function(rule)
{
if (length(rule) >= 3)
as.numeric(trim(rule[3]))
else
1.0
})
factors.tmp <- lapply(factors,matchNames)
# create list of all gene names in both sides of the functions
genes <- unique(c(targets,unname(unlist(factors.tmp))))
isProbabilistic <- (length(unique(targets)) < length(targets))
if (symbolic)
{
if (isProbabilistic)
stop("Probabilistic networks cannot be loaded with symbolic=TRUE!")
interactions <- lapply(factors, function(rule)parseBooleanFunction(rule, genes))
onlyInputs <- setdiff(genes,targets)
if(length(onlyInputs) > 0)
# some genes are only used as inputs, but are not involved in the network
# -> create dummy input and function
{
for(gene in onlyInputs)
{
warning(paste("There is no transition function for gene \"",
gene,"\"! Assuming an input!",sep=""))
interactions[[gene]] = parseBooleanFunction(gene, genes)
}
}
delays <- apply(sapply(interactions,maxTimeDelay,genes=genes),1,max)
names(interactions) <- genes
fixed <- as.integer(sapply(interactions, function(int)
{
if (int$type == "constant")
int$value
else
-1L
}))
names(fixed) <- genes
res <- list(genes = genes, interactions=interactions, fixed=fixed)
res$internalStructs <- .Call("constructNetworkTrees_R",res)
res$timeDelays <- delays
class(res) <- "SymbolicBooleanNetwork"
return(res)
}
else
{
# extract "real" gene names from the list, drop constants
#suppressWarnings(genes <- genes[is.na(as.integer(genes))])
fixed <- rep(-1,length(genes))
names(fixed) <- genes
interactions <- list()
for(i in seq_along(targets))
{
target <- targets[i]
interaction <- generateInteraction(factors[i], genes);
if (isProbabilistic)
{
interaction$probability <- probabilities[i]
interactions[[target]][[length(interactions[[target]]) + 1]] <- interaction
}
else
{
if (length(interaction$func) == 1)
# this is a constant gene => fix it
{
fixed[target] <- interaction$func
}
interactions[[target]] <- interaction
}
}
onlyInputs <- setdiff(genes,targets)
if(length(onlyInputs) > 0)
# some genes are only used as inputs, but are not involved in the network
# -> create dummy input and function
{
for(gene in onlyInputs)
{
warning(paste("There is no transition function for gene \"",
gene,"\"! Assuming an input!",sep=""))
if (isProbabilistic)
interactions[[gene]][[1]] = list(input = length(interactions)+1,func=c(0,1),
expression = gene, probability=1.0)
else
interactions[[gene]] = list(input = length(interactions)+1,func=c(0,1),
expression = gene)
}
}
if (isProbabilistic)
{
wrongProb <- sapply(interactions,function(interaction)
{
abs(1.0-sum(sapply(interaction,function(func)func$probability))) > 0.0001
})
if (any(wrongProb))
stop(paste("The probabilities of gene(s) ",paste(genes[wrongProb],collapse=", ")," do not sum up to 1!",sep=""))
}
res <- list(interactions = interactions,
genes = genes,
fixed = fixed)
if (isProbabilistic)
class(res) <- "ProbabilisticBooleanNetwork"
else
class(res) <- "BooleanNetwork"
return(res)
}
}
matchNames <- function(rule)
{
regexpr <- "([_a-zA-Z][_a-zA-Z0-9]*)[,| |\\)|\\||\\&|\\[]"
rule <- paste(gsub(" ", "", rule, fixed=TRUE)," ",sep="")
res <- unique(unname(sapply(regmatches(rule,gregexpr(regexpr, rule))[[1]],
function(m)
{
sapply(regmatches(m,regexec(regexpr,m)),function(x)x[2])
})))
# remove operators
isOp <- sapply(res, function(x)
{
tolower(x) %in% c("all", "any",
"sumis", "sumgt", "sumlt",
"maj", "timegt", "timelt", "timeis")
})
return(res[!isOp])
}
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