def parse_fidarestat_data():
"""
Convert fidarestat data into simple text file, with columns
gene = Ensembl
C = non-diabetic controls
D = diabetic untreated
F = diabetic + fidarestat
"""
book = xlrd.open_workbook(os.path.join(PATH, 'fidarestat.xls'))
sbml = om.read_sbml(os.path.join(PATH, RAMBO_NAME))
gene_list = om.get_list_of_genes(sbml)
sheet = book.sheet_by_name("maxd1antilog2")
file = os.path.join(PATH, 'fidarestat.txt')
with open(file, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(['gene', 'C', 'D', 'F'])
done = []
for row in range(1, sheet.nrows):
F = strip_string(sheet.cell(row, 3).value)
C = strip_string(sheet.cell(row, 4).value)
D = strip_string(sheet.cell(row, 5).value)
gene = strip_string(sheet.cell(row, 17).value)
if (gene in gene_list) and (gene not in done):
done.append(gene)
writer.writerow([gene, C, D, F])
for gene in gene_list:
if gene not in done:
done.append(gene)
writer.writerow([gene, 0, 0, 0])
def parse_timecourse_data():
"""
Convert timecourse data into simple text file, with columns
gene = Ensembl
C4 = controls after 4 weeks
D4 = diabetic after 4 weeks
C8 = controls after 4 weeks
D8 = diabetic after 4 weeks
"""
book = xlrd.open_workbook(os.path.join(PATH, 'timecourse.xls'))
sbml = om.read_sbml(os.path.join(PATH, RAMBO_NAME))
gene_list = om.get_list_of_genes(sbml)
sheet = book.sheet_by_name("E-MEXP-515-processed-data-13435")
file = os.path.join(PATH, 'timecourse.txt')
all = np.array([])
with open(file, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(['gene', 'C4', 'D4', 'C8', 'D8'])
done = []
data = []
for row in range(2, sheet.nrows):
C4 = strip_string(sheet.cell(row, 9).value)
if not C4:
C4 = 0
D4 = strip_string(sheet.cell(row, 17).value)
if not D4:
D4 = 0
C8 = strip_string(sheet.cell(row, 25).value)
if not C8:
C8 = 0
D8 = strip_string(sheet.cell(row, 33).value)
if not D8:
D8 = 0
gene = strip_string(sheet.cell(row, 1).value)
if (gene in gene_list) and (gene not in done):
done.append(gene)
writer.writerow([gene, C4, D4, C8, D8])
for X in [C4, D4, C8, D8]:
x = float(X)
if x:
data.append(x)
for gene in gene_list:
if gene not in done:
done.append(gene)
writer.writerow([gene, 0, 0, 0, 0])
print 'data range:\nmin:\t%g\nmean:\t%g\nmax:\t%g\n' %(np.min(data), np.mean(data), np.max(data))
def parse_liver_data():
"""
Convert liver data into simple text file, with columns
gene = Ensembl
C = non-diabetic controls
D = diabetic untreated
CT = control + drug
DT = diabetic + drug
Csd = control standard deviation
.. etc
"""
book = xlrd.open_workbook(os.path.join(PATH, 'liver.xls'))
sbml = om.read_sbml(os.path.join(PATH, RAMBO_NAME))
gene_list = om.get_list_of_genes(sbml)
sheet1 = book.sheet_by_name("Means")
sheet2 = book.sheet_by_name("Stdevs")
file = os.path.join(PATH, 'liver.txt')
with open(file, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(['gene', 'C', 'Csd', 'D', 'Dsd', 'CT', 'CTsd', 'DT', 'DTsd'])
done = []
for row in range(1, sheet1.nrows):
gene = strip_string(sheet1.cell(row, 0).value)
D = strip_string(sheet1.cell(row, 1).value)
C = strip_string(sheet1.cell(row, 2).value)
DT = strip_string(sheet1.cell(row, 3).value)
CT = strip_string(sheet1.cell(row, 4).value)
Dsd = strip_string(sheet2.cell(row, 1).value)
Csd = strip_string(sheet2.cell(row, 2).value)
DTsd = strip_string(sheet2.cell(row, 3).value)
CTsd = strip_string(sheet2.cell(row, 4).value)
if (gene in gene_list) and (gene not in done):
done.append(gene)
writer.writerow([gene, C, Csd, D, Dsd, CT, CTsd, DT, DTsd])
for gene in gene_list:
if gene not in done:
done.append(gene)
writer.writerow([gene, 0, 0, 0, 0, 0, 0, 0, 0])
