def main():
basedir, clinical_dir, outdir = get_options()
data_files = os.listdir(basedir)
data_files = util.remove_extraneous_files(data_files)
data_files_by_cancer_type = {
util.get_cancer_type(f): f
for f in data_files
}
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
inputs = []
for clinical in clinical_files:
cancer_type = clinical.split('.')[0]
data_file = data_files_by_cancer_type[cancer_type]
# make_zscores(os.path.join(basedir, data_file),
# os.path.join(clinical_dir, clinical),
# outdir)
inputs.append((os.path.join(basedir, data_file),
os.path.join(clinical_dir, clinical), outdir))
p = Pool(10)
p.map(multiprocess_zscores, inputs)
def main():
clinical_dir, output_dir, header_file = get_options()
headers = pd.read_csv(header_file, index_col=0, header=None)
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
zscore_data = {}
for f in clinical_files:
clinical_path = os.path.join(clinical_dir, f)
cancer_type = util.get_cancer_type(f)
purity_header = headers.get_value(cancer_type, 1)
clinical = util.get_clinical_data(clinical_path,
extra_rows=[purity_header])
cox_dict = analysis.do_cox(clinical.time, clinical.censor,
clinical[purity_header])
zscore_data[cancer_type] = cox_dict
purity_mean = clinical[purity_header].mean()
print purity_mean
clinical['km_split'] = np.where(clinical[purity_header] <= purity_mean,
0, 1)
analysis.do_km(cancer_type, clinical.time, clinical.censor,
clinical.km_split, output_dir)
out_df = pd.DataFrame(zscore_data).transpose()
out_df.to_csv(os.path.join(output_dir, 'simple_purity_zscores.csv'))
def main():
indir, outdir = get_options()
directories = os.listdir(indir)
print indir, outdir
directories = util.remove_extraneous_files(directories)
directories.remove('output')
for d in directories[2:]:
print d
cna_glob = os.path.join(indir, d, '*.cnv.*')
print cna_glob
cna_file = glob.glob(cna_glob)[0]
cna = pd.read_csv(cna_file, index_col=0, sep=util.get_sep_from_filename(cna_file))
clinical_glob = os.path.join(indir, d, '*clinical.*')
clinical_file = glob.glob(clinical_glob)[0]
clinical = pd.read_csv(clinical_file, sep=util.get_sep_from_filename(clinical_file), index_col=0)
clinical = clinical[['Time', 'Censor']]
mut_glob = os.path.join(indir, d, '*mutations*')
mut_file = glob.glob(mut_glob)[0]
mut = pd.read_csv(mut_file, sep=util.get_sep_from_filename(mut_file), low_memory=False)
mutations = prep_mutations(d, mut, clinical)
do_single_cancer_type_cna(d, clinical, cna, outdir)
do_single_cancer_type_mutation(d, clinical, mutations, outdir)
def main(argv=None):
mutation_dir, clinical_dir, outdir = get_options()
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
clinical_files = [os.path.join(clinical_dir, f) for f in clinical_files]
p = Pool(1)
args = []
pancan = {}
for clinical in clinical_files:
cancer_type = util.get_cancer_type(clinical)
print cancer_type
mutation = glob.glob(
os.path.join(mutation_dir, '*' + cancer_type + '*'))[0]
clinical_data = util.get_clinical_data(clinical)
#args.append((mutation, clinical_data, outdir))
pancan[cancer_type] = calculate_cox(mutation, clinical_data, outdir)
#print args
#p.map(multiprocess_zscores, args)
pancan_df = pd.DataFrame(pancan)
pancan_df = pancan_df.transpose()
pancan_df.to_csv(os.path.join(outdir, 'pancan.csv'))
def main(argv=None):
cn_change_size_dir, clinical_dir, outdir = get_options()
input_files = os.listdir(cn_change_size_dir)
input_files = util.remove_extraneous_files(input_files)
input_files = [os.path.join(cn_change_size_dir, i) for i in input_files]
zscore_inputs = []
results = []
for input_file in input_files:
cancer_type = os.path.split(input_file)[1].split('_')[0]
gene = os.path.split(input_file)[1].split('_')[1].split('.')[0]
print cancer_type, gene
# zscore_inputs.append([input_file, cancer_type, gene])
results.append(multiprocess_zscores([input_file, cancer_type, gene]))
#p = Pool(4)
#results = p.map(multiprocess_zscores, zscore_inputs)
with open(os.path.join(outdir, 'cox_any_change_results.csv'), 'w') as out:
formatstr = '{},{},{},{}\n'
out.write('Cancer Type,Gene,Z Score,Count\n')
for cox_dict in results:
cancer_type_gene = cox_dict.keys()[0]
print cancer_type_gene
print cox_dict[cancer_type_gene]
d = cox_dict[cancer_type_gene]
out.write(
formatstr.format(
cancer_type_gene.split('_')[0],
cancer_type_gene.split('_')[1], d['z'],
d['any_change_count']))
def main():
clinical_dir, output_dir, extra_data_dir = get_options()
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
zscore_data = {}
for f in clinical_files:
clinical_path = os.path.join(clinical_dir, f)
cancer_type = util.get_cancer_type(f)
if cancer_type == 'COADREAD':
extra_data = prep_extra_data(extra_data_dir, 'COAD')
else:
extra_data = prep_extra_data(extra_data_dir, cancer_type)
clinical = util.get_clinical_data(clinical_path)
clinical = clinical.join(extra_data)
purity_header = 'Purity_InfiniumPurify'
cox_dict = analysis.do_cox(clinical.time, clinical.censor,
clinical[purity_header])
zscore_data[cancer_type] = cox_dict
purity_mean = clinical[purity_header].mean()
print purity_mean
clinical['km_split'] = np.where(clinical[purity_header] <= purity_mean,
0, 1)
analysis.do_km(cancer_type, clinical.time, clinical.censor,
clinical.km_split, output_dir)
out_df = pd.DataFrame(zscore_data).transpose()
out_df.to_csv(
os.path.join(output_dir, 'add_data_simple_purity_zscores.csv'))
def main(argv=None):
mutation_dir, clinical_dir, outdir, univariate_output = get_options()
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
clinical_files = [os.path.join(clinical_dir, f) for f in clinical_files]
p = Pool(16)
args = []
for clinical in clinical_files:
cancer_type = util.get_cancer_type(clinical)
print cancer_type
mutation = glob.glob(os.path.join(mutation_dir, '*' + cancer_type + '*'))[0]
univariate_file = None
if univariate_output:
univariate_file = glob.glob(os.path.join(univariate_output, cancer_type, cancer_type + '.zscores.out.csv'))[0]
print univariate_file
clinical_data = util.get_clinical_data(clinical)
cancer_type_outdir = os.path.join(outdir, cancer_type)
if not os.path.isdir(cancer_type_outdir):
os.makedirs(cancer_type_outdir)
args.append((mutation, clinical_data, cancer_type_outdir, univariate_file))
# calculate_cox(mutation, clinical_data, cancer_type_outdir, univariate_file=univariate_file)
print args
p.map(multiprocess_zscores, args)
def main(argv=None):
mutation_dir, clinical_dir, outdir, tumor_stage_dir = get_options()
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
clinical_files = [os.path.join(clinical_dir, f) for f in clinical_files]
p = Pool(16)
args = []
for clinical in clinical_files:
cancer_type = util.get_cancer_type(clinical)
print cancer_type
mutation = glob.glob(
os.path.join(mutation_dir, '*' + cancer_type + '*'))[0]
tumor_stage = os.path.join(tumor_stage_dir,
cancer_type + '_clinical.csv')
if not os.path.isfile(tumor_stage):
continue
clinical_data = util.get_clinical_data(clinical)
cancer_type_outdir = os.path.join(outdir, cancer_type)
if not os.path.isdir(cancer_type_outdir):
os.makedirs(cancer_type_outdir)
args.append((mutation, clinical_data, tumor_stage, cancer_type_outdir))
# calculate_cox(mutation, clinical_data, tumor_stage, cancer_type_outdir)
p.map(multiprocess_zscores, args)
def main():
indir, outdir, split_files = get_options()
files = os.listdir(os.path.join(indir, 'cnas'))
files = util.remove_extraneous_files(files)
criteria_list = [stouffer_sig_and_two_zscore_sig]
for criteria in criteria_list:
if not split_files:
outfile_c = open(os.path.join(outdir, criteria.__name__ + '.out.csv'), 'w')
outfile_m = outfile_c
for f in files:
cancer_type = f.split('_')[0]
if split_files:
outfile_c = os.path.join(outdir, cancer_type + '_CNA_.criteria_met.out.csv')
outfile_m = os.path.join(outdir, cancer_type + '_MUT_.criteria_met.out.csv')
cna_cancer_type_criteria_met = apply_criteria(os.path.join(indir, 'cnas', f),
criteria, 'cna')
cna_cancer_type_criteria_met.index = 'cna_' + cna_cancer_type_criteria_met.index
cna_cancer_type_criteria_met.to_csv(outfile_c, index_label='CNA_'+cancer_type)
mut_cancer_type_criteria_met = apply_criteria(os.path.join(indir, 'mutations', f),
criteria, 'mut')
print f, mut_cancer_type_criteria_met.index
mut_cancer_type_criteria_met.index = 'mut_' + mut_cancer_type_criteria_met.index
mut_cancer_type_criteria_met.to_csv(outfile_m, index_label='MUT_'+cancer_type)
if not split_files:
outfile.close()
def main():
copy_number_loc, clinical, outdir = get_options()
cnas = os.listdir(copy_number_loc)
cnas = util.remove_extraneous_files(cnas)
results = pd.DataFrame()
for c in cnas:
cancer_type = util.get_cancer_type(c)
print cancer_type
clinical_file = glob.glob(
os.path.join(clinical, '*' + cancer_type + '*.txt'))[0]
clin = util.get_clinical_data(clinical_file)
patient_breaks = count_breaks(os.path.join(copy_number_loc, c))
patient_breaks = patient_breaks.reset_index()
patient_breaks = util.maybe_clear_non_01s(patient_breaks, 'Sample',
cancer_type)
patient_breaks = util.add_identifier_column(patient_breaks, 'Sample')
patient_breaks = patient_breaks.set_index('identifier')
patient_breaks = patient_breaks.drop('Sample', axis=1)
breaks_and_clin = patient_breaks.join(clin, how='inner')
breaks_and_clin.to_csv(
os.path.join(outdir, cancer_type + '_breaks.csv'))
cox = analysis.do_cox(breaks_and_clin.time, breaks_and_clin.censor,
breaks_and_clin.breaks)
cox['cancer_type'] = cancer_type
results = results.append(cox, ignore_index=True)
results.to_csv(os.path.join(outdir, 'cox_results.csv'))
def main(argv=None):
cnv_dir, mutation_dir, clinical_dir, gene_list, outdir = get_options()
gene_list = pd.read_csv(gene_list, header=None)
gene_list = '\'' + gene_list[0]
cnv_files = os.listdir(cnv_dir)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_files = [os.path.join(cnv_dir, i) for i in cnv_files]
mutation_results = pd.DataFrame()
cnv_results = pd.DataFrame()
for cnv in cnv_files:
cancer_type = util.get_cancer_type(cnv)
print cancer_type
mutation = glob.glob(os.path.join(mutation_dir, cancer_type + '*'))[0]
clinical = glob.glob(
os.path.join(clinical_dir, '*' + cancer_type + '*'))[0]
cnv_results = cnv_results.append(
make_cnv_zscores(cnv, clinical, gene_list))
mutation_results = mutation_results.append(
make_mutation_zscores(mutation, clinical, gene_list))
mutation_results.to_csv(os.path.join(
outdir, 'mutation_zscores_w_hazards_fig1.csv'),
index=False)
cnv_results.to_csv(os.path.join(outdir, 'cnv_zscores_w_hazards_fig1.csv'),
index=False)
def main(argv=None):
cnv_dir, rna, mutation_dir, clinical_dir, outdir, input_file = get_options()
cnv_files = os.listdir(cnv_dir)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_files = [os.path.join(cnv_dir, i) for i in cnv_files]
interesting_genes = pd.read_csv(input_file, comment='#')
interesting_genes['Gene'] = '\'' + interesting_genes['Gene']
zscore_inputs = []
corr_results = []
for cnv in cnv_files:
cancer_type = util.get_cancer_type(cnv)
cancer_type_genes = interesting_genes[interesting_genes['Cancer Type'] == cancer_type]
if len(cancer_type_genes) == 0:
continue
print cancer_type
print cancer_type_genes
rnaseq = glob.glob(os.path.join(rna, cancer_type + '*'))[0]
mutation = glob.glob(os.path.join(mutation_dir, cancer_type + '*'))[0]
clinical = glob.glob(os.path.join(clinical_dir, '*' + cancer_type + '*'))[0]
zscore_inputs.append([cnv, rnaseq, mutation, clinical, outdir, cancer_type_genes])
# corr_results.append(multiprocess_data([cnv, rnaseq, mutation, clinical, outdir, cancer_type_genes]))
p = Pool(4)
corr_results = p.map(multiprocess_data, zscore_inputs)
df = pd.concat(corr_results, verify_integrity=True, axis=1)
print df
df.to_csv(os.path.join(outdir, 'corr_results.csv'))
def all_cancer_types(copy_number_dir,
annotation_file,
outdir,
parallel_workers=0):
copy_number_files = os.listdir(copy_number_dir)
copy_number_files = util.remove_extraneous_files(copy_number_files)
# returns a dataframe indexed by gene name, with chr number and txstart
args = []
annotation_data = process_annotation_file(annotation_file)
for c in copy_number_files:
infile = os.path.join(copy_number_dir, c)
type_name = os.path.basename(infile).split('.')[0]
outfile = os.path.join(outdir, type_name + '.cnv.csv')
if parallel_workers == 0:
# returns a dict of patient_ids => lists of interval trees containing range data for each chromosome
patient_data = process_input_file(infile)
process_and_write_data(outfile, annotation_data, patient_data)
else:
args.append((infile, outfile, annotation_data))
if parallel_workers > 0:
p = multiprocessing.Pool(parallel_workers)
p.map(multiprocess, args)
def main():
clinical_dir, tumor_groups = get_options()
files = os.listdir(clinical_dir)
files = util.remove_extraneous_files(files)
for f in files:
cancer_type = util.get_cancer_type(f)
clinical_file = os.path.join(clinical_dir, f)
tumor_group_file = os.path.join(tumor_groups, cancer_type + '.csv')
count_tumor_groups(clinical_file, tumor_group_file)
def main():
basedir, clinical_dir, hypermutated_patients, outdir = get_options()
hypermutated = pd.read_csv(hypermutated_patients, header=None, names=['patients'])
data_files = os.listdir(basedir)
data_files = util.remove_extraneous_files(data_files)
data_files_by_cancer_type = {util.get_cancer_type(f): f for f in data_files}
clinical_files = os.listdir(clinical_dir)
clinical_files = util.remove_extraneous_files(clinical_files)
inputs = []
for clinical in clinical_files:
cancer_type = clinical.split('.')[0]
data_file = data_files_by_cancer_type[cancer_type]
make_zscores(os.path.join(basedir, data_file),
os.path.join(clinical_dir, clinical),
hypermutated,
outdir)
def main(argv=None):
cnv_dir, mutation_dir, clinical_dir, outdir = get_options()
cnv_files = os.listdir(cnv_dir)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_files = [os.path.join(cnv_dir, i) for i in cnv_files]
zscore_inputs = []
for cnv in cnv_files:
cancer_type = util.get_cancer_type(cnv)
mutation = glob.glob(os.path.join(mutation_dir, cancer_type + '*'))[0]
clinical = glob.glob(
os.path.join(clinical_dir, '*' + cancer_type + '*'))[0]
zscore_inputs.append([cnv, mutation, clinical, outdir])
p = Pool(4)
p.map(multiprocess_zscores, zscore_inputs)
def main():
indir, clinical_dir, outdir = get_options()
files = os.listdir(indir)
files = util.remove_extraneous_files(files)
for f in files:
cancer_type = get_cbioportal_cancer_type(f)
print cancer_type
clinical_file = os.path.join(clinical_dir,
cancer_type + '_clinical.csv')
cancer_type_outdir = os.path.join(outdir, cancer_type)
if not os.path.isdir(cancer_type_outdir):
os.makedirs(cancer_type_outdir)
clinical = get_cbioportal_clinical(clinical_file)
calculate_zscores_for_file(os.path.join(indir, f), clinical,
cancer_type_outdir, cancer_type)
def main(argv=None):
if argv is None:
argv = sys.argv
input_directory, clinical_directory, outdir = get_options()
cnv_files = os.listdir(input_directory)
cnv_files = util.remove_extraneous_files(cnv_files)
for cnv in cnv_files:
cancer_type = get_cbioportal_cancer_type(cnv)
print cancer_type
clinical_file = glob.glob(os.path.join(clinical_directory, '*' + cancer_type + '*'))[0]
outglob = glob.glob(os.path.join(outdir, cancer_type + '*'))
if len(outglob) == 0:
print cancer_type
make_zscores(os.path.join(input_directory, cnv), clinical_file, outdir)
def main(argv=None):
indir, clinical_dir, outdir = get_options()
files = os.listdir(indir)
files = util.remove_extraneous_files(files)
for copy_number in files:
cancer_type = get_icgc_cancer_type(copy_number)
print cancer_type
clinical_file = os.path.join(clinical_dir, cancer_type + '.csv')
relevant_clinical = pd.read_csv(clinical_file,
index_col=0,
low_memory=False)[['Time', 'Censor'
]].astype(float)
make_zscores(os.path.join(indir, copy_number), relevant_clinical,
outdir)
def main(argv=None):
cnv_dir, mutation_dir, clinical_dir, outdir, input_file = get_options()
cnv_files = os.listdir(cnv_dir)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_files = [os.path.join(cnv_dir, i) for i in cnv_files]
interesting_genes = pd.read_csv(input_file, comment='#')
print interesting_genes
interesting_genes['Gene'] = '\'' + interesting_genes['Gene']
zscore_inputs = []
for cnv in cnv_files:
cancer_type = util.get_cancer_type(cnv)
cancer_type_genes = interesting_genes[interesting_genes['Cancer Type']
== cancer_type]
if len(cancer_type_genes) == 0:
continue
print cancer_type
print cancer_type_genes
mutation = glob.glob(os.path.join(mutation_dir, cancer_type + '*'))[0]
clinical = glob.glob(
os.path.join(clinical_dir, '*' + cancer_type + '*'))[0]
zscore_inputs.append(
[cnv, mutation, clinical, outdir, cancer_type_genes])
#multiprocess_zscores([cnv, mutation, clinical, outdir, cancer_type_genes])
p = Pool(4)
results = p.map(multiprocess_zscores, zscore_inputs)
print results
with open(os.path.join(outdir, 'cox_results.csv'), 'w') as out:
formatstr = '{},{},{},{},{},{},{},{}\n'
out.write(
'Cancer Type,Gene,CNA Z Score, CNA P value, Mutation Z score, Mutation P Value, Mutation Count, n\n'
)
for coxs in results:
cancer_type = coxs.keys()[0]
print cancer_type
for gene, cox_dict in coxs[cancer_type].iteritems():
print gene, cox_dict
out.write(
formatstr.format(cancer_type, gene, cox_dict['var-z'],
cox_dict['var-p'],
cox_dict[gene + '_mutations-z'],
cox_dict[gene + '_mutations-p'],
cox_dict['mutation_count'],
cox_dict['var-n']))
def main():
indir, outdir = get_options()
clinical_files = os.listdir(indir)
clinical_files = util.remove_extraneous_files(clinical_files)
stage_row = 'patient.stage_event.pathologic_stage'
for clinical_f in clinical_files:
f = os.path.join(indir, clinical_f)
cancer_type = util.get_cancer_type(clinical_f)
stage_row = tumor_stage_util.TUMOR_STAGE[cancer_type]
if stage_row:
clinical = util.get_clinical_data(f,
extra_rows=[stage_row],
extra_rows_numeric=False)
clinical[stage_row] = clinical[stage_row].str.strip()
print cancer_type
print clinical[stage_row].value_counts()
def main(argv=None):
cnv_dir, clinical_dir, outdir, input_file = get_options()
cnv_files = os.listdir(cnv_dir)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_files = [os.path.join(cnv_dir, i) for i in cnv_files]
interesting_genes = pd.read_csv(input_file, comment='#')
interesting_genes['Gene'] = '\'' + interesting_genes['Gene']
zscore_inputs = []
for cnv in cnv_files:
cancer_type = util.get_cancer_type(cnv)
cancer_type_genes = interesting_genes[interesting_genes['Cancer Type'] == cancer_type]
if len(cancer_type_genes) == 0:
continue
clinical = glob.glob(os.path.join(clinical_dir, cancer_type + '*'))[0]
multiprocess_copy_number_changes([cnv, clinical, outdir, cancer_type_genes])
def count_codons(data, outdir):
files = os.listdir(data)
files = util.remove_extraneous_files(files)
files.remove('HG36_HG37')
outdata = []
ncbi_outdata = []
for f in files:
file_name = os.path.join(data, f)
cancer_type = util.get_cancer_type(file_name)
codon_counts = count_codons_in_file(file_name)
outdata.append(codon_counts)
df = pd.concat(outdata, axis=1, verify_integrity=True)
df['sum'] = df.sum(axis=1)
df.to_csv('codon_counts.csv',
index_label=[
'Gene', 'Chromosome', 'Start Position', 'Wild Type Allele'
])
def main():
clinical_dir, row_names_file, basedir, interesting_genes, outdir = get_options(
)
files = os.listdir(clinical_dir)
files = util.remove_extraneous_files(files)
clinical_by_cancer_type = {util.get_cancer_type(f): f for f in files}
row_names = pd.read_csv(row_names_file, header=0)
interesting_genes = pd.read_csv(interesting_genes, header=0, index_col=1)
for i, row in row_names.iterrows():
cancer_type = row['cancer_type']
cancer_type_fname = cancer_type
print cancer_type
clinical_file = clinical_by_cancer_type[cancer_type]
clinical_file = os.path.join(clinical_dir, clinical_file)
if row['histological_subtype_row'] != 'EXTERNAL':
clinical_data = make_clinical_data(clinical_file,
row['histological_subtype_row'],
outdir)
else:
subtype_data = prep_BRCA_data(row['external_file'], cancer_type)
# subtype_data.to_csv(os.path.join(outdir, 'BRCA_annotation_subtype_data.csv'))
clinical = util.get_clinical_data(clinical_file)
subtype_clinical = clinical.join(subtype_data['subtype'],
how='outer')
clinical_data = save_subtype_files(subtype_clinical, 'subtype',
cancer_type, outdir)
cancer_type_fname = 'BRCA_HER2'
cna_file = glob.glob(os.path.join(basedir, cancer_type + '*.csv'))[0]
cna = pd.read_csv(cna_file, header=0, index_col=0).T
genes = '\'' + interesting_genes['Gene']
genes = genes.loc[cancer_type]
print genes
if type(genes) == str:
print cna[[genes]]
joined = cna[[genes]].join(clinical_data, how='outer')
else:
joined = cna[genes].join(clinical_data, how='outer')
joined.to_csv(os.path.join(outdir, cancer_type_fname + '.csv'))
def main():
indir, clinical_dir, outdir, hgnc_file = get_options()
files = os.listdir(indir)
files = util.remove_extraneous_files(files)
hgnc = pd.read_csv(hgnc_file, low_memory=False)
hgnc = hgnc[['Approved Symbol', 'Ensembl ID(supplied by Ensembl)']]
hgnc.columns = ['Symbol', 'Ensembl ID']
hgnc.set_index('Ensembl ID', inplace=True)
hgnc['Symbol'] = '\'' + hgnc['Symbol']
for f in files:
cancer_type = get_icgc_cancer_type(f)
print cancer_type
clinical_file = os.path.join(clinical_dir, cancer_type + '.csv')
cancer_type_outdir = os.path.join(outdir, cancer_type)
if not os.path.isdir(cancer_type_outdir):
os.makedirs(cancer_type_outdir)
calculate_zscores_for_file(os.path.join(indir, f), clinical_file,
cancer_type_outdir, hgnc)
def main(argv=None):
cnv_dir, structural_breaks, interesting_genes_file, outdir = get_options()
cnv_files = os.listdir(cnv_dir)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_files = [os.path.join(cnv_dir, i) for i in cnv_files]
interesting_genes = None
if interesting_genes_file:
interesting_genes = pd.read_csv(interesting_genes_file)
zscore_inputs = []
for cnv in cnv_files:
cancer_type = util.get_cancer_type(cnv)
breaks = glob.glob(
os.path.join(structural_breaks, '*' + cancer_type + '*'))[0]
zscore_inputs.append([cnv, breaks, interesting_genes, outdir])
make_cn_zscores(cnv, breaks, interesting_genes, outdir)
p = Pool(4)
p.map(multiprocess_cn_zscores, zscore_inputs)
def main(argv=None):
if argv is None:
argv = sys.argv
input_directory, clinical, outdir, extra_data_dir = get_options()
clinical_files = os.listdir(clinical)
clinical_files = util.remove_extraneous_files(clinical_files)
args = []
for c in clinical_files:
cancer_type = util.get_cancer_type(c)
print cancer_type
clinical_data = util.get_clinical_data(os.path.join(clinical, c))
copy_number = glob.glob(
os.path.join(input_directory, cancer_type + '*.csv'))[0]
args.append((copy_number, clinical_data, extra_data_dir, outdir))
# make_zscores(copy_number, clinical_data, extra_data_dir, outdir)
p = Pool(4)
p.map(multiprocess_zscores, args)
def main(argv=None):
input_directory, clinical_directory, gene_file, outdir = get_options()
gene_list = pd.read_csv(gene_file, header=None)[0].values
cnv_files = os.listdir(input_directory)
cnv_files = util.remove_extraneous_files(cnv_files)
cnv_file = [f for f in cnv_files if 'METABRIC' in f]
cnv = cnv_file[0]
cancer_type = get_cbioportal_cancer_type(cnv)
clinical_file = glob.glob(
os.path.join(clinical_directory, '*' + cancer_type + '*'))[0]
print cancer_type
results = make_zscores(os.path.join(input_directory, cnv), clinical_file,
gene_list)
results_df = pd.DataFrame(results)
results_df = results_df.set_index('gene')
results_df.to_csv(os.path.join(outdir, 'metabric_copy_number.csv'))
def main(argv=None):
if argv is None:
argv = sys.argv
input_directory, clinical, outdir, extra_clinical_rows_file = get_options()
clinical_files = os.listdir(clinical)
clinical_files = util.remove_extraneous_files(clinical_files)
all_extra_clinical_rows = pd.read_csv(extra_clinical_rows_file, index_col=0, header=None)
for c in clinical_files:
cancer_type = util.get_cancer_type(c)
extra_rows = [all_extra_clinical_rows.loc[cancer_type][1]]
print cancer_type
clinical_data = util.get_clinical_data(os.path.join(clinical, c),
extra_rows=extra_rows)
print clinical_data
copy_number = glob.glob(os.path.join(input_directory, cancer_type + '*.csv'))[0]
print copy_number
make_zscores(copy_number, clinical_data, outdir, extra_rows)
def main():
clinical_dir, row_names_file, outdir = get_options()
files = os.listdir(clinical_dir)
files = util.remove_extraneous_files(files)
clinical_by_cancer_type = {util.get_cancer_type(f): f for f in files}
row_names = pd.read_csv(row_names_file, header=0)
for i, row in row_names.iterrows():
cancer_type = row['cancer_type']
print cancer_type
clinical_file = clinical_by_cancer_type[cancer_type]
clinical_file = os.path.join(clinical_dir, clinical_file)
if row['histological_subtype_row'] != 'EXTERNAL':
make_clinical_data(clinical_file, row['histological_subtype_row'], outdir)
else:
subtype_data = prep_BRCA_data(row['external_file'], cancer_type)
subtype_data.to_csv(os.path.join(outdir, 'BRCA_annotation_subtype_data.csv'))
clinical = util.get_clinical_data(clinical_file)
subtype_clinical = clinical.join(subtype_data['subtype'], how='outer')
save_subtype_files(subtype_clinical, 'subtype', cancer_type, outdir)
