def getDataPackage():
dd = "/home/earls3/Price/AUREAPackage/src/AUREA/data/"
softfile = dd+"GDS2545.soft.gz"
gnfile = dd+"c2.biocarta.v2.5.symbols.gmt"
synfile = dd+"Homo_sapiens.gene_info.gz"
gnf = GMTParser.GMTParser(gnfile)
sp = SOFTParser.SOFTParser(softfile)
normal = []
tumor = []
for line in sp.column_heading_info[0]:
if string.find(line[1], 'normal prostate tissue free') > 0:
normal.append(line[0].strip())
elif string.find(line[1], 'tumor') > 0:
tumor.append(line[0].strip())
dt = DataCleaner.DataTable()
dt.getSOFTData(sp)
dp = DataPackager.dataPackager()
dp.addGeneNetwork(gnf.getAllNetworks())
dp.addDataTable(dt)
dp.addSynonyms(synfile)
dp.createClassification("Tumor")
dp.createClassification("Normal")
for samp in tumor:
dp.addToClassification("Tumor", dt.dt_id, samp)
for samp in normal[:-1]:
dp.addToClassification("Normal", dt.dt_id, samp)
dp.setUnclassified(dt.dt_id, normal[-1])
return dp
def buildData(file1, file2, config):
"""
Takes the 2 csv file names and the config object and returns the datapackage
"""
gnfile = "c2.biocarta.v2.5.symbols.gmt"
synfile = "Homo_sapiens.gene_info.gz"
collision = config.getSetting("datatable", "Gene Collision Rule")[0]
bad_data = config.getSetting("datatable", "Bad Data Value")[0]
gene_column = config.getSetting("datatable", "Gene Column")[0]
probe_column = config.getSetting("datatable", "Probe Column")[0]
gnf = GMTParser.GMTParser(gnfile)
#VC: edit here
#create GEO Data Getter
#f1=GEODataGetter()
f1 = CSVParser.CSVParser(file1, probe_column_name=probe_column, gene_column_name=gene_column)
f2 = CSVParser.CSVParser(file2, probe_column_name=probe_column, gene_column_name=gene_column)
#create a data table
dt1 = DataCleaner.DataTable(probe_column, gene_column, collision, bad_data)
dt1.getCSVData(f1)
dt2 = DataCleaner.DataTable(probe_column, gene_column, collision, bad_data)
dt2.getCSVData(f2)
#VC: done edit
dp = DataPackager.dataPackager(merge_cache=".")
dp.addGeneNetwork(gnf.getAllNetworks())
dp.addSynonyms(synfile)
#add data table
dp.addDataTable(dt1)
dp.addDataTable(dt2)
#create subsets(classes)
dp.createClassification("f1")
for samp in f1.getDataColumnHeadings():
dp.addToClassification("f1", dt1.dt_id, samp)
dp.createClassification("f2")
for samp in f2.getDataColumnHeadings():
dp.addToClassification("f2", dt2.dt_id, samp)
return dp