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require 'matrix'
class Dataset
def initialize file
@dir = File.dirname file
@dependent_variable_type = File.read(File.join(@dir,"dependent_variable_type")).chomp
if @dependent_variable_type == "binary"
@dependent_variable_values = {}
File.readlines(File.join(@dir,"dependent_variable_values")).each_with_index{|v,i| @dependent_variable_values[v.chomp] = i}
end
@independent_variable_type = File.read(File.join(@dir,"independent_variable_type")).chomp
@lines = File.readlines(file)
@header = @lines.shift.split(",")
@header.first.match(/ID/i) ? @has_id = true : @has_id = false
@dependent_variable_name = @header.pop
@ids = []
@dependent_variables = []
@independent_variables = []
@independent_variable_names = []
end
def print_variables
File.open(File.join(@dir,"ids"),"w+") { |f| f.puts @ids.join("\n") }
File.open(File.join(@dir,"dependent_variable_name"),"w+") { |f| f.puts @dependent_variable_name }
File.open(File.join(@dir,"dependent_variables"),"w+") { |f| f.puts @dependent_variables.join("\n") }
File.open(File.join(@dir,"independent_variable_names"),"w+") { |f| f.puts @independent_variable_names.join(",") }
File.open(File.join(@dir,"independent_variables"),"w+") { |f| @independent_variables.each{|row| f.puts row.join(",")} }
end
def scale_independent_variables file
@header.shift if @has_id
@independent_variable_names = @header
@lines.each_with_index do |line,i|
items = line.chomp.split(",")
@ids << items.shift
if @dependent_variable_type == "binary"
@dependent_variables << @dependent_variable_values[items.pop]
elsif @dependent_variable_type == "numeric"
@dependent_variables << items.pop.to_f
end
@independent_variables << items.collect{|i| i.to_f}
end
@independent_variables = Matrix[ *@independent_variables ]
columns = @independent_variables.column_vectors
@independent_variable_means = columns.collect{|c| c.to_a.mean}
@independent_variable_standard_deviations = columns.collect{|c| c.to_a.standard_deviation}
scaled_columns = []
columns.each_with_index{|col,i| scaled_columns << col.collect{|v| v ? (v-@independent_variable_means[i])/@independent_variable_standard_deviations[i] : nil}}
@independent_variables = Matrix.columns(scaled_columns).to_a
print_variables
File.open(File.join(@dir,"means"),"w+") { |f| f.puts @independent_variable_means.join(",") }
File.open(File.join(@dir,"standard_deviations"),"w+") { |f| f.puts @independent_variable_standard_deviations.join(",") }
end
def fingerprint_independent_variables file, fingerprint_type="MP2D"
fingerprints = []
@lines.each_with_index do |line,i|
items = line.chomp.split(",")
@has_id ? @ids << items.shift : @ids << i
if @dependent_variable_type == "binary"
@dependent_variables << @dependent_variable_values[items.pop]
elsif @dependent_variable_type == "numeric"
@dependent_variables << items.pop.to_f
end
@independent_variables << [items[0]] + Compound.new(items[0]).fingerprint(fingerprint_type)
end
@independent_variable_names = ["Canonical Smiles"] + fingerprints.flatten.sort.uniq
print_variables
end
end
=begin
# Create a dataset from SDF file
# files with a single data field are read as BioActivities (i.e. dependent variable)
# files with multiple data fields are read as SubstanceProperties (i.e. independent variable)
# @param [File]
# @return [OpenTox::Dataset]
def self.from_sdf_file file
md5 = Digest::MD5.hexdigest(File.read(file)) # use hash to identify identical files
dataset = self.find_by(:md5 => md5)
if dataset
$logger.debug "Found #{file} in the database (id: #{dataset.id}, md5: #{dataset.md5}), skipping import."
else
$logger.debug "Parsing #{file}."
dataset = self.new(:source => file, :name => File.basename(file,".*"), :md5 => md5)
original_id = OriginalId.find_or_create_by(:dataset_id => dataset.id,:name => dataset.name+".ID")
read_result = false
sdf = ""
feature_name = ""
compound = nil
features = {}
table = [["ID","SMILES"]]
File.readlines(file).each do |line|
if line.match %r{\$\$\$\$}
sdf << line
id = sdf.split("\n").first.chomp
compound = Compound.from_sdf sdf
row = [id,compound.smiles]
features.each do |f,v|
table[0] << f unless table[0].include? f
row[table[0].index(f)] = v
end
table << row
sdf = ""
features = {}
elsif line.match /^>\s+</
feature_name = line.match(/^>\s+<(.*)>/)[1]
read_result = true
else
if read_result
value = line.chomp
features[feature_name] = value
read_result = false
else
sdf << line
end
end
end
dataset.parse_table table
end
dataset
end
# Create a dataset from PubChem Assay
# @param [Integer] PubChem AssayID (AID)
# @return [OpenTox::Dataset]
def self.from_pubchem_aid aid
# TODO get regression data
aid_url = File.join PUBCHEM_URI, "assay/aid/#{aid}"
assay_metadata = JSON.parse(RestClientWrapper.get(File.join aid_url,"description/JSON").to_s)["PC_AssayContainer"][0]["assay"]["descr"]
name = assay_metadata["name"].gsub(/\s+/,"_")
dataset = self.new(:source => aid_url, :name => name)
# Get assay data in chunks
# Assay record retrieval is limited to 10000 SIDs
# https://pubchemdocs.ncbi.nlm.nih.gov/pug-rest-tutorial$_Toc458584435
list = JSON.parse(RestClientWrapper.get(File.join aid_url, "sids/JSON?list_return=listkey").to_s)["IdentifierList"]
listkey = list["ListKey"]
size = list["Size"]
start = 0
csv = []
while start < size
url = File.join aid_url, "CSV?sid=listkey&listkey=#{listkey}&listkey_start=#{start}&listkey_count=10000"
csv += CSV.parse(RestClientWrapper.get(url).to_s).select{|r| r[0].match /^\d/} # discard header rows
start += 10000
end
table = [["SID","SMILES",name]]
csv.each_slice(100) do |slice| # get SMILES in chunks
cids = slice.collect{|s| s[2]}
pubchem_cids = []
JSON.parse(RestClientWrapper.get(File.join(PUBCHEM_URI,"compound/cid/#{cids.join(",")}/property/CanonicalSMILES/JSON")).to_s)["PropertyTable"]["Properties"].each do |prop|
i = cids.index(prop["CID"].to_s)
value = slice[i][3]
if value == "Active" or value == "Inactive"
table << [slice[i][1].to_s,prop["CanonicalSMILES"],slice[i][3].to_s]
pubchem_cids << prop["CID"].to_s
else
dataset.warnings << "Ignoring CID #{prop["CID"]}/ SMILES #{prop["CanonicalSMILES"]}, because PubChem activity is #{value}."
end
end
(cids-pubchem_cids).each { |cid| dataset.warnings << "Could not retrieve SMILES for CID #{cid}, all entries are ignored." }
end
dataset.parse_table table
dataset
end
# Convert dataset to SDF format
# @return [String] SDF string
def to_sdf
export_features = merged_features
export_features = transformed_bioactivity_features if export_features.empty?
export_features = bioactivity_features if export_features.empty?
export_feature = export_features.first
sdf = ""
compounds.each do |compound|
sdf_lines = compound.sdf.sub(/\$\$\$\$\n/,"").split("\n")
sdf_lines[0] = compound.smiles
sdf += sdf_lines.join("\n")
sdf += "\n> <#{export_feature.name}>\n"
sdf += values(compound,export_feature).uniq.join ","
sdf += "\n"
sdf += "\n$$$$\n"
end
sdf
end
# Merge an array of datasets
# @param [Array<OpenTox::Dataset>] datasets Datasets to be merged
# @param [Array<OpenTox::Feature>] features Features to be merged (same size as datasets)
# @param [Array<Hash>] value_maps Value transfomations (use nil for keeping original values, same size as dataset)
# @param [Bool] keep_original_features Copy original features/values to the merged dataset
# @param [Bool] remove_duplicates Delete duplicated values (assuming they come from the same experiment)
# @return [OpenTox::Dataset] merged dataset
def self.merge datasets: , features: , value_maps: , keep_original_features: , remove_duplicates:
dataset = self.create(:source => datasets.collect{|d| d.id.to_s}.join(", "), :name => datasets.collect{|d| d.name}.uniq.join(", ")+" merged")
datasets.each do |d|
dataset.data_entries += d.data_entries
dataset.warnings += d.warnings
end if keep_original_features
feature_classes = features.collect{|f| f.class}.uniq
merged_feature = nil
if feature_classes.size == 1
if features.first.kind_of? NominalFeature
merged_feature = MergedNominalBioActivity.find_or_create_by(:name => features.collect{|f| f.name}.uniq.join(" and ") + " merged", :original_feature_ids => features.collect{|f| f.id}, :transformations => value_maps)
else
merged_feature = MergedNumericBioActivity.find_or_create_by(:name => features.collect{|f| f.name} + " merged", :original_feature_ids => features.collect{|f| f.id}) # TODO: regression transformations
end
else
raise ArgumentError, "Cannot merge features of different types (#{feature_classes})."
end
accept_values = []
features.each_with_index do |f,i|
dataset.data_entries += datasets[i].data_entries.select{|de| de[1] == f.id}.collect do |de|
value_maps[i] ? v = value_maps[i][de[2]] : v = de[2]
accept_values << v
[de[0],merged_feature.id,v]
end
end
if merged_feature.is_a? MergedNominalBioActivity
merged_feature.accept_values = accept_values.uniq.sort
merged_feature.save
end
dataset.data_entries.uniq! if remove_duplicates
dataset.save
dataset
end
end
end
=end
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