if len(common_genes) != len(genes):
warnings.warn('All input genes were not found in data. The missing genes '
'are {}'.format(missing_genes),
category=Warning)
# Incorporate copy number for gene activation/inactivation
if copy_number:
copy_loss_df = pd.read_table(copy_loss_file, index_col=0)
copy_gain_df = pd.read_table(copy_gain_file, index_col=0)
# Load cancer gene classification table
cancer_genes = pd.read_table(cancer_gene_file)
mut_subset_df = integrate_copy_number(y=mut_subset_df,
cancer_genes_df=cancer_genes,
genes=common_genes,
loss_df=copy_loss_df,
gain_df=copy_gain_df)
# Add covariate info to y_matrix
mut_subset_df = mut_subset_df.assign(total_status=mut_subset_df.max(axis=1))
mut_subset_df = mut_subset_df.reset_index().merge(sample_freeze_df,
left_on='SAMPLE_BARCODE',
right_on='SAMPLE_BARCODE')\
.set_index('SAMPLE_BARCODE')
y_burden_matrix = mut_burden_df.merge(pd.DataFrame(mut_subset_df.total_status),
right_index=True,
left_on='SAMPLE_BARCODE')\
.set_index('SAMPLE_BARCODE')
y_sub = mut_subset_df.loc[y_burden_matrix.index]['DISEASE']
if copy_number:
# Load copy number matrices
copy_loss_df = pd.read_table(os.path.join('data',
'copy_number_loss_status.tsv'),
index_col=0)
copy_gain_df = pd.read_table(os.path.join('data',
'copy_number_gain_status.tsv'),
index_col=0)
# Load cancer gene classification table
cancer_genes = pd.read_table(
os.path.join('data', 'vogelstein_cancergenes.tsv'))
y = integrate_copy_number(y=y,
cancer_genes_df=cancer_genes,
genes=common_genes,
loss_df=copy_loss_df,
gain_df=copy_gain_df,
include_mutation=no_mutation)
# Process y matrix
y = y.assign(total_status=y.max(axis=1))
y = y.reset_index().merge(sample_freeze,
how='left').set_index('SAMPLE_BARCODE')
count_df = y.groupby('DISEASE').sum()
prop_df = count_df.divide(y['DISEASE'].value_counts(sort=False).sort_index(),
axis=0)
count_table = count_df.merge(prop_df, left_index=True, right_index=True)
count_table.to_csv(count_table_file)
# Filter diseases