def collect_data(infile, genes):
sample_data = defaultdict(dict)
with open(infile, "rU") as casefile:
reader = csv.reader(casefile, dialect="excel-tab")
reader.next()
for row in reader:
if row[0].startswith("#"):
sys.stderr.write("WARNING: Skipping commented input line\n")
continue
print row
profile_data = cbioportal.get_multi_gene(row[1], row[2], genes)
clin_data = cbioportal.get_clin_data(row[1])
profile_header = profile_data.pop(0)
clin_header = clin_data.pop(0)
profile_header_data = profile_header.split()
profile_header_data.pop(0)
profile_header_data.pop(0)
if len(profile_data) <= 0:
sys.stderr.write(
"ERROR: No profile data retrieved for query, " "with response header: {}\n".format(profile_header)
)
continue
if len(clin_data) <= 0:
sys.stderr.write(
"ERROR: No clinical data retrieved for query, " "with response header: {}\n".format(clin_header)
)
continue
for line in profile_data:
data = line.split()
data.pop(0)
gene_id = data.pop(0)
# print gene_id
i = 0
for value in data:
# sys.stdout.write("{}: {}\n".format(profile_header_data[i], value))
sample_data[profile_header_data[i]][gene_id] = value
i += 1
for line in clin_data:
if not line.strip():
continue
data = line.split()
sample_data[data[0]]["AGE"] = data[1]
return sample_data
def collect_data(infile, genes):
sample_data = defaultdict(dict)
with open(infile, 'rU') as casefile:
reader = csv.reader(casefile, dialect='excel-tab')
reader.next()
for row in reader:
if row[0].startswith("#"):
sys.stderr.write("WARNING: Skipping commented input line\n")
continue
print row
profile_data = cbioportal.get_multi_gene(row[1], row[2], genes)
clin_data = cbioportal.get_clin_data(row[1])
profile_header = profile_data.pop(0)
clin_header = clin_data.pop(0)
profile_header_data = profile_header.split()
profile_header_data.pop(0)
profile_header_data.pop(0)
if len(profile_data) <= 0:
sys.stderr.write("ERROR: No profile data retrieved for query, "
"with response header: {}\n".format(profile_header))
continue
if len(clin_data) <= 0:
sys.stderr.write("ERROR: No clinical data retrieved for query, "
"with response header: {}\n".format(clin_header))
continue
for line in profile_data:
data = line.split()
data.pop(0)
gene_id = data.pop(0)
# print gene_id
i = 0
for value in data:
# sys.stdout.write("{}: {}\n".format(profile_header_data[i], value))
sample_data[profile_header_data[i]][gene_id] = value
i += 1
for line in clin_data:
if not line.strip():
continue
data = line.split()
sample_data[data[0]]['AGE'] = data[1]
return sample_data
parser.add_argument('-c', '--cases', help="Input file with study, case, and profile ids [Required]")
parser.add_argument('-g', '--genes', help="Text file with list of genes to evaluate for correlations")
parser.add_argument('-o', '--output', help='Output file name')
args = parser.parse_args()
with open(args.genes, 'rU') as genefile:
genes = genefile.read().splitlines()
with open(args.output, 'w') as outfile:
outfile.write("Study\tCase List\tProfile\tGenes\tNumbers\tSpearman's Rho\tP-Value\n")
with open(args.cases, 'rU') as casefile:
reader = csv.reader(casefile, dialect='excel-tab')
reader.next()
for row in reader:
profile_data = cbioportal.get_multi_gene(row[1], row[2], genes)
header = profile_data.pop(0)
for pair in itertools.combinations(profile_data, 2):
data1 = pair[0].split()
data2 = pair[1].split()
# Remove Gene ID
data1.pop(0)
data2.pop(0)
gene1 = data1.pop(0)
gene2 = data2.pop(0)
expression1 = list()
expression2 = list()
outfile.write(
"Study\tCase List\tProfile\tGenes\tMin Exp\t25th Pctl\tMedian\t75th Pctl\tMax\t"
"Bin1 (Low) #\tBin1 R\tBin1 p\t"
"Bin2 #\tBin2 R\tBin2 p\t"
"Bin3 #\tBin3 R\tBin3 p\t"
"Bin4 (High) #\tBin4 R\tBin4 p\n")
with open(args.cases, 'rU') as casefile:
reader = csv.reader(casefile, dialect='excel-tab')
reader.next()
for row in reader:
if row[0].startswith("#"):
sys.stderr.write(
"WARNING: Skipping commented input line\n")
continue
profile_data = cbioportal.get_multi_gene(row[1], row[2], genes)
header = profile_data.pop(0)
if len(profile_data) <= 0:
sys.stderr.write(
"ERROR: No data retrieved for query, with response header: {}\n"
.format(header))
continue
# Because gene expression data is returned in alphabetical order we have to find the line
# containing our control gene data line, remove it from the data_lines, and isolated it for comparisons
primary_data = list()
i = 0
for line in profile_data:
# print line
data = line.split()