LBMC » NucleoMiner

# get_content = function(# Get content from cached

# ### Acces to the cached content of a file via the global variable NM_CACHE. Load content in NM_CACHE if needed.

# obj, ##<< The object that we want its content.

# ... ##<< Parameters that will be passed to my_read

# ) {

#   UseMethod("get_content", obj)

#   ### Returns the cached content of the current object.

# }

#

# get_content.default = structure( function(# Get content from cached

# ### Acces to the cached content of a file via the global variable NM_CACHE. Load content in NM_CACHE if needed.

# obj, ##<< The object that we want its content.

# ... ##<< Parameters that will be passed to my_read

# ) {

#   if (inherits(try(NM_CACHE,TRUE), "try-error") || is.null(NM_CACHE)) {

#     NM_CACHE <<- list()

#   }

#   if (is.null(NM_CACHE[[obj$filename]])) {

#     print(paste("Loading file ",obj$filename, sep=""))

#     tmp_content = my_read(obj, ...)

#     print("affect it...")

#     NM_CACHE[[obj$filename]] <<- tmp_content

#     print("done.")

#   }

#   return(NM_CACHE[[obj$filename]])

# ### Returns the cached content of the current object.

# }, ex=function(){

#   # Create a dataframe

#   df = NULL

#   df = dfadd(df, list(key1 = "value1", key2 = "value2"))

#   df = dfadd(df, list(key1 = "value1'", key2 = "value3'"))

#   # Dump it into tmp file

#   write.table(df, file="/tmp/tmp.dump.table.tmp")

#   # Load it into cache using feature.

#   cached_content_object  = list(filename="/tmp/tmp.dump.table.tmp")

#   class(cached_content_object) = "table"

#   # First time it will be load into cache

#   print(get_content(cached_content_object))

#   # Second time not

#   print(get_content(cached_content_object))

#

# })

#

# my_read = function(

# ### Abstract my_read function.

#   obj, ...) {

#   UseMethod("my_read", obj)

# }

#

# my_read.default = function(

# ### Default my_read function.

#   obj, ...){

#   stop(paste("ERROR, my_read is not defined for any Objects (file ", obj$filename," )", sep=""))

# }

#

# my_read.fasta = function(

# ### my_read function for fasta files.

#   obj, ...){

#   # require(seqinr)

#   return(read.fasta(obj$filename, ...))

# }

#

# my_read.table = function(

# ### my_read function for table files.

#   obj, ...){

#   if (rev(unlist(strsplit(obj$filename, ".", fixed=TRUE)))[1] == "gz") {

#     return(read.table(file=gzfile(obj$filename), ...))

#   } else {

#     return(read.table(file=obj$filename, ...))

#   }

# }

#

# my_read.cvs = function(

# ### my_read function for cvs files.

#   obj, ...){

#   return(read.csv(file=obj$filename, ...))

# }

### This function aggregates nucleosomes from replicated samples. It uses TemplateFilter ouput of each sample as replicate. Each sample owns a set of nucleosomes computed using TemplateFilter and ordered by the position of their center (dyad). A chain of nucleosomes is builts across all replicates. Adjacent nucleosomes of the chain are compared two by two. Comparison is based on a log likelihood ratio (LLR1). depending on the LLR1 value nucleosomes are merged (low LLR) or separated (high LLR). Finally the function returns a list of clusters and all computed llr_scores. Each cluster ows an attribute wp for “well positioned”. This attribute is set to TRUE if the cluster is composed of exactly one nucleosome of each sample.

	print(paste(sum(reads$snep_index), " SNEPs found for ", length(reads[,1])," nucs and ", fdr*100,"% of FDR.", sep = ""))

            foo<<-nucs