def get_gistic_genes(data_path, cancer, filter_with_rna=True,
collapse_on_bands=True, min_patients=5):
"""
Gets a matrix of events for high grade amplifications and homozygous
deletions.
We filter down this list by asserting that a copy number event corresponds
with a resultant expression change.
The final matrix merges gene-level events on the same band to combine
redundant events and reduce the test space.
"""
gistic = FH.get_gistic_gene_matrix(data_path, cancer, '01')
deletion = gistic[(gistic == -2).sum(1) > min_patients]
amp = gistic[(gistic == 2).sum(1) > min_patients]
ft = pd.MultiIndex.from_tuples # rediculously long pandas names
deletion.index = ft([('Deletion', s[0], s[2]) for s in deletion.index])
amp.index = ft([('Amplification', s[0], s[2]) for s in amp.index])
if filter_with_rna:
rna = FH.read_rnaSeq(data_path, cancer)
deletion = rna_filter(deletion, -2, rna)
amp = rna_filter(amp, 2, rna)
cna_genes = amp.append(deletion)
if collapse_on_bands == False:
return cna_genes
cna_genes = pd.DataFrame({(a[0], a[1], tuple(b.index.get_level_values(2))):
b.mean().round() for a, b in
cna_genes.groupby(level=[0, 1])}).T
cna_genes.index = pd.MultiIndex.from_tuples(cna_genes.index)
return cna_genes
def get_gistic_genes(data_path,
cancer,
filter_with_rna=True,
collapse_on_bands=True,
min_patients=5):
'''
Gets a matrix of events for high grade amplifications and homozygous
deletions.
We filter down this list by asserting that a copy number event corresponds
with a resultant expression change.
The final matrix merges gene-level events on the same band to combine
redundant events and reduce the test space.
'''
gistic = FH.get_gistic_gene_matrix(data_path, cancer, '01')
deletion = gistic[(gistic == -2).sum(1) > min_patients]
amp = gistic[(gistic == 2).sum(1) > min_patients]
ft = pd.MultiIndex.from_tuples # rediculously long pandas names
deletion.index = ft([('Deletion', s[0], s[2]) for s in deletion.index])
amp.index = ft([('Amplification', s[0], s[2]) for s in amp.index])
if filter_with_rna:
rna = FH.read_rnaSeq(data_path, cancer)
deletion = rna_filter(deletion, -2, rna)
amp = rna_filter(amp, 2, rna)
cna_genes = amp.append(deletion)
if collapse_on_bands == False:
return cna_genes
cna_genes = pd.DataFrame({(a[0], a[1], tuple(b.index.get_level_values(2))):
b.mean().round()
for a, b in cna_genes.groupby(level=[0, 1])}).T
cna_genes.index = pd.MultiIndex.from_tuples(cna_genes.index)
return cna_genes
def get_global_vars(data_path, cancer, patients=None):
"""
Get compiled DataFrame of global molecular variables from Firehose
data. Returns a feature by patient DataFrame with (data-type, variable)
on the columns and patient barcodes on the index.
"""
try:
data_matrix = FH.read_rnaSeq(data_path, cancer, patients)
U, S, vH = frame_svd(data_matrix)
exp_pc = pd.DataFrame({'pc1': vH[0], 'pc2': vH[1]})
except:
exp_pc = pd.DataFrame()
try:
data_matrix = read_methylation(data_path, cancer, patients)
U, S, vH = frame_svd(data_matrix)
meth_pc = pd.DataFrame({'pc1': vH[0], 'pc2': vH[1]})
except:
meth_pc = pd.DataFrame()
try:
meth_age, amar = 'FAIL', 'FAIL'
# meth_age, amar = get_age_signal(data_path, cancer)
meth_pc = meth_pc.join(meth_age).join(amar)
print 'Should probably check this out'
except:
pass
cna_rates = get_cna_rates(data_path, cancer, patients)
mutation_rates = get_mutation_rates(data_path, cancer, patients)
gv = pd.concat([exp_pc, meth_pc, cna_rates, mutation_rates],
keys=['mRNASeq', 'methylation', 'cna', 'mutation'], axis=1)
gv = gv.dropna(how='all', axis=1)
return gv
def get_global_vars(data_path, cancer, patients=None):
'''
Get compiled DataFrame of global molecular variables from Firehose
data. Returns a feature by patient DataFrame with (data-type, variable)
on the columns and patient barcodes on the index.
'''
try:
data_matrix = FH.read_rnaSeq(data_path, cancer, patients)
U, S, vH = frame_svd(data_matrix)
exp_pc = pd.DataFrame({'pc1': vH[0], 'pc2': vH[1]})
except:
exp_pc = pd.DataFrame()
try:
data_matrix = read_methylation(data_path, cancer, patients)
U, S, vH = frame_svd(data_matrix)
meth_pc = pd.DataFrame({'pc1': vH[0], 'pc2': vH[1]})
except:
meth_pc = pd.DataFrame()
try:
meth_age, amar = 'FAIL', 'FAIL'
# meth_age, amar = get_age_signal(data_path, cancer)
meth_pc = meth_pc.join(meth_age).join(amar)
print 'Should probably check this out'
except:
pass
cna_rates = get_cna_rates(data_path, cancer, patients)
mutation_rates = get_mutation_rates(data_path, cancer, patients)
gv = pd.concat([exp_pc, meth_pc, cna_rates, mutation_rates],
keys=['mRNASeq', 'methylation', 'cna', 'mutation'],
axis=1)
gv = gv.dropna(how='all', axis=1)
return gv
